@article{Nielsen2018,
abstract = {BACKGROUND AND PURPOSE: Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume. METHODS: Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNNdeep) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNNdeep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNNTmax), a shallow CNN based on a combination of 9 different biomarkers (CNNshallow), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600x10(-6) mm(2)/s (ADCthres). To assess whether CNNdeep is capable of differentiating outcomes of +/-intravenous rtPA, patients not receiving intravenous rtPA were included to train CNNdeep,-rtpa to access a treatment effect. The networks' performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast. RESULTS: CNNdeep yields significantly better performance in predicting final outcome (AUC=0.88+/-0.12) than generalized linear model (AUC=0.78+/-0.12; P=0.005), CNNTmax (AUC=0.72+/-0.14; P{\textless}0.003), and ADCthres (AUC=0.66+/-0.13; P{\textless}0.0001) and a substantially better performance than CNNshallow (AUC=0.85+/-0.11; P=0.063). Measured by contrast, CNNdeep improves the predictions significantly, showing superiority to all other methods (P{\textless}/=0.003). CNNdeep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different (P=0.048). CONCLUSIONS: The considerable prediction improvement accuracy over current state of the art increases the potential for automated decision support in providing recommendations for personalized treatment plans.},
author = {Nielsen, Anne and Hansen, Mikkel Bo and Tietze, Anna and Mouridsen, Kim},
doi = {10.1161/STROKEAHA.117.019740},
issn = {1524-4628 (Electronic)},
journal = {Stroke},
keywords = {Adult,Aged,Aged, 80 and over,Brain Ischemia,Deep Learning,Diffusion Magnetic Resonance Imaging,Female,Fibrinolytic Agents,Humans,Male,Middle Aged,ROC Curve,Stroke,Tissue Plasminogen Activator,Young Adult,administration {\&} dosage,drug therapy,methods,therapeutic use},
language = {eng},
month = {jun},
number = {6},
pages = {1394--1401},
pmid = {29720437},
title = {{Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.}},
volume = {49},
year = {2018}
}

@article{Zuley2016,
author = {Zuley, M. L. and Jarosz, R. and Drake, B. F. and Rancilio, D. and Klim, A. and Rieger-Christ, K. and Lemmerman, J.},
doi = {10.7937/K9/TCIA.2016.YXOGLM4Y},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Prostate Adenocarcinoma [TCGA-PRAD] collection}},
year = {2016}
}

@article{Pedano2016,
author = {Pedano, N. and Flanders, A. E. and Scarpace, L. and Mikkelsen, T. and Eschbacher, J. M. and Hermes, B. and Ostrom, Q.},
doi = {10.7937/K9/TCIA.2016.L4LTD3TK},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Low Grade Glioma [TCGA-LGG] collection}},
year = {2016}
}

@article{Vallieres2015,
abstract = {This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.},
author = {Valli{\`{e}}res, M and Freeman, C R and Skamene, S R and {El Naqa}, I},
doi = {10.1088/0031-9155/60/14/5471},
file = {::},
isbn = {1361-6560 (Electronic)$\backslash$r0031-9155 (Linking)},
issn = {0031-9155},
journal = {Physics in Medicine and Biology},
mendeley-groups = {MIDL2020},
number = {14},
pages = {5471--5496},
pmid = {26119045},
title = {{A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities}},
url = {http://stacks.iop.org/0031-9155/60/i=14/a=5471?key=crossref.4ebd805d5c2e9543c94ce4172981418e},
volume = {60},
year = {2015}
}


@article{Schmainda2016,
author = {Schmainda, Kathleen M and Prah, Melissa A and Connelly, Jennifer M and Rand, Scott D},
doi = {10.7937/K9/TCIA.2016.5DI84Js8},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs [ Dataset ]}},
year = {2016}
}


@article{Erickson2016c,
author = {Erickson, B. J. and Mutch, D. and Lippmann, L. and Jarosz, R.},
doi = {10.7937/K9/TCIA.2016.GKJ0ZWAC},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) collection}},
year = {2016}
}


@article{Lucchesi2016b,
author = {Lucchesi, F. R. and Aredes, N. D.},
doi = {10.7937/K9/TCIA.2016.GDHL9KIM},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Stomach Adenocarcinoma [TCGA-STAD] collection}},
year = {2016}
}

@article{CPTAC-CM,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/k9/tcia.2018.odu24gze},
journal = {The Cancer Imaging Archive.},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma [CPTAC-CM] collection [Data set]}},
year = {2018}
}

@misc{russakovsky2014imagenet,
    title={ImageNet Large Scale Visual Recognition Challenge},
    author={Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
    year={2014},
    eprint={1409.0575},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{he2015deep,
    title={Deep Residual Learning for Image Recognition},
    author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
    year={2015},
    eprint={1512.03385},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@article{Clark2013,
abstract = {The National Institutes of Health have placed significant emphasis on sharing of  research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.},
author = {Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David and Pringle, Michael and Tarbox, Lawrence and Prior, Fred},
doi = {10.1007/s10278-013-9622-7},
issn = {1618-727X (Electronic)},
journal = {Journal of digital imaging},
keywords = {Diagnostic Imaging,Female,Humans,Information Storage and Retrieval,Male,Medical Informatics,Multimodal Imaging,National Cancer Institute (U.S.),Neoplasms,Program Evaluation,Quality Control,Radiology Information Systems,Software,United States,diagnosis,methods,organization {\&} administration},
language = {eng},
mendeley-groups = {MIDL2020},
month = {dec},
number = {6},
pages = {1045--1057},
pmid = {23884657},
title = {{The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.}},
volume = {26},
year = {2013}
}


@article{Gulshan2016,
abstract = {Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting: A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. Exposure: Deep learning-trained algorithm. Main Outcomes and Measures: The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. Results: The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2{\%} women; prevalence of RDR, 683/8878 fully gradable images [7.8{\%}]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6{\%} women; prevalence of RDR, 254/1745 fully gradable images [14.6{\%}]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95{\%} CI, 0.988-0.993) for EyePACS-1 and 0.990 (95{\%} CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3{\%} (95{\%} CI, 87.5{\%}-92.7{\%}) and the specificity was 98.1{\%} (95{\%} CI, 97.8{\%}-98.5{\%}). For Messidor-2, the sensitivity was 87.0{\%} (95{\%} CI, 81.1{\%}-91.0{\%}) and the specificity was 98.5{\%} (95{\%} CI, 97.7{\%}-99.1{\%}). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5{\%} and specificity was 93.4{\%} and for Messidor-2 the sensitivity was 96.1{\%} and specificity was 93.9{\%}. Conclusions and Relevance: In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.},
author = {Gulshan, Varun and Peng, Lily and Coram, Marc and Stumpe, Martin C and Wu, Derek and Narayanaswamy, Arunachalam and Venugopalan, Subhashini and Widner, Kasumi and Madams, Tom and Cuadros, Jorge and Kim, Ramasamy and Raman, Rajiv and Nelson, Philip C and Mega, Jessica L and Webster, Dale R},
doi = {10.1001/jama.2016.17216},
issn = {1538-3598 (Electronic)},
journal = {JAMA},
keywords = {Algorithms,Diabetic Retinopathy,Female,Fundus Oculi,Humans,Machine Learning,Macular Edema,Male,Middle Aged,Neural Networks, Computer,Observer Variation,Ophthalmologists,Photography,Sensitivity and Specificity,diagnostic imaging},
language = {eng},
mendeley-groups = {MIDL2020},
month = {dec},
number = {22},
pages = {2402--2410},
pmid = {27898976},
title = {{Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.}},
volume = {316},
year = {2016}
}

@article{Holback2016,
author = {Holback, C. and Jarosz, R. and Prior, F. and Mutch, D. G. and Bhosale, P. and Garcia, K. and Erickson, B. J.},
doi = {10.7937/K9/TCIA.2016.NDO1MDFQ},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Ovarian Cancer [TCGA-OV] collection}},
year = {2016}
}


@article{Lucchesi2016,
author = {Lucchesi, F. R. and Aredes, N. D.},
doi = {10.7937/K9/TCIA.2016.SQ4M8YP4},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma [TCGA-CESC] collection}},
year = {2016}
}

@article{Kirk2016b,
author = {Kirk, S. and Lee, Y. and Kumar, P. and Filippini, J. and Albertina, B. and Watson, M. and Lemmerman, J.},
doi = {10.7937/K9/TCIA.2016.TYGKKFMQ},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Lung Squamous Cell Carcinoma [TCGA-LUSC] collection}},
year = {2016}
}

@article{CPTAC-SAR,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/TCIA.2019.9bt23r95},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Sarcomas [CPTAC-SAR] collection [Data set]}},
year = {2018}
}

@article{Albertina2016,
author = {Albertina, B. and Watson, M. and Holback, C. and Jarosz, R. and Kirk, S. and Lee, Y. and Lemmerman, J.},
doi = {10.7937/K9/TCIA.2016.JGNIHEP5},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Lung Adenocarcinoma [TCGA-LUAD] collection}},
year = {2016}
}

@article{Kirk2016a,
author = {Kirk, S. and Lee, Y. and Sadow, C. A. and Levine, S. and Roche, C. and Bonaccio, E. and Filiippini, J.},
doi = {10.7937/K9/TCIA.2016.HJJHBOXZ},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Colon Adenocarcinoma}},
year = {2016}
}

@article{Armato2015,
author = {Samuel G Armato and Hadjiiski, Lubomir M and Tourassi, Georgia D and Drukker, Karen and Giger, Maryellen L and Li, Feng and Redmond, George and Farahani, Keyvan and Kirby, Justin S and Clarke, Laurence P},
doi = {10.1117/1.JMI.2.2.020103},
journal = {Journal of Medical Imaging},
mendeley-groups = {MIDL2020},
number = {2},
pages = {1--5},
publisher = {SPIE},
title = {{Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned}},
url = {https://doi.org/10.1117/1.JMI.2.2.020103},
volume = {2},
year = {2015}
}

@article{Lucchesi2016a,
author = {Lucchesi, F. R. and Aredes, N. D.},
doi = {10.7937/K9/TCIA.2016.VPTNRGFY},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Esophageal Carcinoma [TCGA-ESCA] collection}},
year = {2016}
}

@article{Kirk2016,
author = {Kirk, S. and Lee, Y. and Lucchesi, F. R. and Aredes, N. D. and Gruszauskas, N. and Catto, J. and Lemmerman, J.},
doi = {10.7937/K9/TCIA.2016.8LNG8XDR},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Urothelial Bladder Carcinoma [TCGA-BLCA] collection}},
year = {2016}
}

@article{Lingle2016,
author = {Lingle, W. and Erickson, B. J. and Zuley, M. L. and Jarosz, R. and Bonaccio, E. and Filippini, J. and Gruszauskas, N.},
doi = {10.7937/K9/TCIA.2016.AB2NAZRP},
mendeley-groups = {MIDL2020},
title = {{Data from The Cancer Genome Atlas Breast Invasive Carcinoma [TCGA-BRCA] collection}},
year = {2016}
}

@misc{rajpurkar2017chexnet,
    title={CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning},
    author={Pranav Rajpurkar and Jeremy Irvin and Kaylie Zhu and Brandon Yang and Hershel Mehta and Tony Duan and Daisy Ding and Aarti Bagul and Curtis Langlotz and Katie Shpanskaya and Matthew P. Lungren and Andrew Y. Ng},
    year={2017},
    eprint={1711.05225},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@article{rider2009,
abstract = { PurposeTo evaluate the variability of tumor unidimensional, bidimensional, and volumetric measurements on same-day repeat computed tomographic (CT) scans in patients with non–small cell lung cancer. Materials and MethodsThis HIPAA–compliant study was approved by the institutional review board, with informed patient consent. Thirty-two patients with non–small cell lung cancer, each of whom underwent two CT scans of the chest within 15 minutes by using the same imaging protocol, were included in this study. Three radiologists independently measured the two greatest diameters of each lesion on both scans and, during another session, measured the same tumors on the first scan. In a separate analysis, computer software was applied to assist in the calculation of the two greatest diameters and the volume of each lesion on both scans. Concordance correlation coefficients (CCCs) and Bland-Altman plots were used to assess the agreements between the measurements of the two repeat scans (reproducibility) and between the two repeat readings of the same scan (repeatability). ResultsThe reproducibility and repeatability of the three radiologists' measurements were high (all CCCs, ≥0.96). The reproducibility of the computer-aided measurements was even higher (all CCCs, 1.00). The 95{\%} limits of agreements for the computer-aided unidimensional, bidimensional, and volumetric measurements on two repeat scans were (−7.3{\%}, 6.2{\%}), (−17.6{\%}, 19.8{\%}), and (−12.1{\%}, 13.4{\%}), respectively. ConclusionChest CT scans are well reproducible. Changes in unidimensional lesion size of 8{\%} or greater exceed the measurement variability of the computer method and can be considered significant when estimating the outcome of therapy in a patient.{\textcopyright} RSNA, 2009 },
annote = {PMID: 19561260},
author = {Zhao, Binsheng and James, Leonard P and Moskowitz, Chaya S and Guo, Pingzhen and Ginsberg, Michelle S and Lefkowitz, Robert A and Qin, Yilin and Riely, Gregory J and Kris, Mark G and Schwartz, Lawrence H},
doi = {10.1148/radiol.2522081593},
journal = {Radiology},
mendeley-groups = {MIDL2020},
number = {1},
pages = {263--272},
title = {{Evaluating Variability in Tumor Measurements from Same-day Repeat CT Scans of Patients with Non–Small Cell Lung Cancer}},
url = {https://doi.org/10.1148/radiol.2522081593},
volume = {252},
year = {2009}
}


@article{Scarpace2015,
author = {Scarpace, Lisa and Flanders, Adam E. and Jain, Rajan and Mikkelsen, Tom and Andrews, David W.},
doi = {10.7937/K9/TCIA.2015.588OZUZB},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Data From REMBRANDT}},
year = {2015}
}


@article{Kalpathy-Cramer2016,
abstract = {Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 mul to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p {\textless} 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p {\textless} 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p {\textless} 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.},
author = {Kalpathy-Cramer, Jayashree and Zhao, Binsheng and Goldgof, Dmitry and Gu, Yuhua and Wang, Xingwei and Yang, Hao and Tan, Yongqiang and Gillies, Robert and Napel, Sandy},
doi = {10.1007/s10278-016-9859-z},
issn = {1618-727X (Electronic)},
journal = {Journal of digital imaging},
keywords = {Algorithms,Humans,Lung Neoplasms,Phantoms, Imaging,Reproducibility of Results,Solitary Pulmonary Nodule,Tomography, X-Ray Computed,Tumor Burden,diagnostic imaging,pathology},
language = {eng},
mendeley-groups = {MIDL2020},
month = {aug},
number = {4},
pages = {476--487},
pmid = {26847203},
title = {{A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.}},
volume = {29},
year = {2016}
}

@article{Akin2016,
author = {Akin, O. and Elnajjar, P. and Heller, M. and Jarosz, R. and Erickson, B. J. and Kirk, S. and Filippini, J},
doi = {10.7937/K9/TCIA.2016.V6PBVTDR},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma [TCGA-KIRC] collection}},
year = {2016}
}


@article{Linehan2016,
author = {Linehan, M. and Gautam, R. and Kirk, S. and Lee, Y. and Roche, C. and Bonaccio, E. and Jarosz, R.},
doi = {10.7937/K9/TCIA.2016.ACWOGBEF},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{The Cancer Genome Atlas Cervical Kidney renal papillary cell carcinoma [KIRP] collection}},
year = {2016}
}

@article{Erickson2016b,
author = {Erickson, B. J. and Kirk, S. and Lee, Y. and Bathe, O. and Kearns, M. and Gerdes, C. and Lemmerman, J.},
doi = {10.7937/K9/TCIA.2016.IMMQW8UQ},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collection}},
year = {2016}
}


@article{Linehan2016a,
author = {Linehan, M. W. and Gautam, R. and Sadow, C. A. and Levine, S.},
doi = {10.7937/K9/TCIA.2016.YU3RBCZN},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Kidney Chromophobe [TCGA-KICH] collection}},
year = {2016}
}

@article{Zuley2016a,
author = {Zuley, M. L. and Jarosz, R. and Kirk, S. and Lee, Y. and Colen, R. and Garcia, K. and Aredes, N. D.},
doi = {10.7937/K9/TCIA.2016.LXKQ47MS},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma [TCGA-HNSC] collection}},
year = {2016}
}

@article{Choyke2016,
author = {Choyke, P and Turkbey, B and Pinto, P and Merino, M and Wood, B},
doi = {10.7937/K9/TCIA.2016.6046GUDv},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Data From PROSTATE-MRI}},
year = {2016}
}

@article{Huang2014,
abstract = {Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.},
author = {Huang, Wei and Li, Xin and Chen, Yiyi and Li, Xia and Chang, Ming-Ching and Oborski, Matthew J and Malyarenko, Dariya I and Muzi, Mark and Jajamovich, Guido H and Fedorov, Andriy and Tudorica, Alina and Gupta, Sandeep N and Laymon, Charles M and Marro, Kenneth I and Dyvorne, Hadrien A and Miller, James V and Barbodiak, Daniel P and Chenevert, Thomas L and Yankeelov, Thomas E and Mountz, James M and Kinahan, Paul E and Kikinis, Ron and Taouli, Bachir and Fennessy, Fiona and Kalpathy-Cramer, Jayashree},
doi = {10.1593/tlo.13838},
issn = {1936-5233},
journal = {Translational oncology},
language = {eng},
mendeley-groups = {MIDL2020},
month = {feb},
number = {1},
pages = {153--166},
publisher = {Neoplasia Press Inc.},
title = {{Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge}},
url = {https://www.ncbi.nlm.nih.gov/pubmed/24772219 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3998693/},
volume = {7},
year = {2014}
}

@article{Scarpace2016,
author = {Scarpace, L. and Mikkelsen, T. and Cha, Soonmee and Rao, S. and Tekchandani, S. and Gutman, D. and Pierce, L. J.},
doi = {10.7937/K9/TCIA.2016.RNYFUYE9},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Glioblastoma Multiforme [TCGA-GBM] collection}},
year = {2016}
}

@ARTICLE{Litjens2014,
author={G. {Litjens} and O. {Debats} and J. {Barentsz} and N. {Karssemeijer} and H. {Huisman}},
journal={IEEE Transactions on Medical Imaging},
title={Computer-Aided Detection of Prostate Cancer in MRI},
year={2014},
volume={33},
number={5},
pages={1083-1092},
keywords={biological organs;biomedical MRI;cancer;feature extraction;image classification;image reconstruction;medical image processing;sensitivity;sensitivity analysis;MRI;cancer death;magnetic resonance imaging;prostate cancer detection;fully automated computer-aided detection system;multiatlas-based prostate segmentation;voxel feature extraction;classification;local maxima detection;second stage segments;cancer likelihoods;pharmacokinetic behavior;magnetic resonance-guided biopsy;reference standard;lesion-based free-response receiver operating characteristic curve;patient-based receiver operating characteristic analysis;prospective clinical performance;radiologists;clinical workflow;sensitivity;high specificity reading setting;screening situations;independent second reader;first-reader setting;Prostate cancer;Image segmentation;Magnetic resonance imaging;Design automation;Feature extraction;Biopsy;Machine learning;Computer-aided detection;image analysis;machine learning;magnetic resonance imaging;prostate cancer;Algorithms;Case-Control Studies;Humans;Image Interpretation, Computer-Assisted;Magnetic Resonance Imaging;Male;Prostate;Prostatic Neoplasms;ROC Curve},
doi={10.1109/TMI.2014.2303821},
ISSN={1558-254X},
month={May},}

@article{Bloch2015a,
author = {Bloch, B. Nicolas and Jain, Ashali and Jaffe, C. Carl},
doi = {10.7937/K9/TCIA.2015.FOQEUJVT},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Data From PROSTATE-DIAGNOSIS}},
year = {2015}
}

@article{Madabhushi2016,
author = {Madabhushi, A. and Feldman, M.},
doi = {10.7937/K9/TCIA.2016.TLPMR1AM},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Fused Radiology-Pathology Prostate Dataset}},
year = {2016}
}

@misc{milletari2016vnet,
    title={V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation},
    author={Fausto Milletari and Nassir Navab and Seyed-Ahmad Ahmadi},
    year={2016},
    eprint={1606.04797},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@inproceedings{Roth2015,
abstract = {Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via P-ConvNet and nearest neighbor fusion. Then we describe a regional ConvNet (R	1−ConvNet) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a ``zoom-out'' fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked R	2−ConvNet leveraging the joint space of CT intensities and the P−ConvNet dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6{\{}$\backslash$textpm{\}}6.3{\%} in training and 71.8{\{}$\backslash$textpm{\}}10.7{\%} in testing.},
address = {Cham},
author = {Roth, Holger R and Lu, Le and Farag, Amal and Shin, Hoo-Chang and Liu, Jiamin and Turkbey, Evrim B and Summers, Ronald M},
booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015},
editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M and Frangi, Alejandro},
isbn = {978-3-319-24553-9},
mendeley-groups = {MIDL2020},
pages = {556--564},
publisher = {Springer International Publishing},
title = {{DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation}},
year = {2015}
}

@article{Yorke2019,
author = {Yorke, Afua A. and McDonald, Gary C. and {Solis Jr.}, David and Guerrero., Thomas},
doi = {10.7937/TCIA.2019.woskq5oo},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Pelvic Reference Data}},
year = {2019}
}


@article{Aerts2014,
abstract = {Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Aerts, H J and Velazquez, E R and Leijenaar, R T and Parmar, C and Grossmann, P and Carvalho, S and Bussink, J and Monshouwer, R and Haibe-Kains, B and Rietveld, D and Hoebers, F and Rietbergen, M M and Leemans, C R and Dekker, A and Quackenbush, J and Gillies, R J and Lambin, P},
doi = {10.1038/ncomms5006},
eprint = {arXiv:1011.1669v3},
file = {::},
isbn = {2041-1723 (Electronic)$\backslash$r2041-1723 (Linking)},
issn = {2041-1723},
journal = {Nature Communications},
mendeley-groups = {Radiology: AI Paper,MIDL2020},
pages = {4006},
pmid = {24892406},
title = {{Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach}},
volume = {5},
year = {2014}
}

@article{Rusu2017,
abstract = {OBJECTIVE: To develop an approach for radiology-pathology fusion of ex vivo histology of surgically excised pulmonary nodules with pre-operative CT, to radiologically map spatial extent of the invasive adenocarcinomatous component of the nodule. METHODS: Six subjects (age: 75 +/- 11 years) with pre-operative CT and surgically excised ground-glass nodules (size: 22.5 +/- 5.1 mm) with a significant invasive adenocarcinomatous component ({\textgreater}5 mm) were included. The pathologist outlined disease extent on digitized histology specimens; two radiologists and a pulmonary critical care physician delineated the entire nodule on CT (in-plane resolution: {\textless}0.8 mm, inter-slice distance: 1-5 mm). We introduced a novel reconstruction approach to localize histology slices in 3D relative to each other while using CT scan as spatial constraint. This enabled the spatial mapping of the extent of tumour invasion from histology onto CT. RESULTS: Good overlap of the 3D reconstructed histology and the nodule outlined on CT was observed (65.9 +/- 5.2{\%}). Reduction in 3D misalignment of corresponding anatomical landmarks on histology and CT was observed (1.97 +/- 0.42 mm). Moreover, the CT attenuation (HU) distributions were different when comparing invasive and in situ regions. CONCLUSION: This proof-of-concept study suggests that our fusion method can enable the spatial mapping of the invasive adenocarcinomatous component from 2D histology slices onto in vivo CT. KEY POINTS: * 3D reconstructions are generated from 2D histology specimens of ground glass nodules. * The reconstruction methodology used pre-operative in vivo CT as 3D spatial constraint. * The methodology maps adenocarcinoma extent from digitized histology onto in vivo CT. * The methodology potentially facilitates the discovery of CT signature of invasive adenocarcinoma.},
author = {Rusu, Mirabela and Rajiah, Prabhakar and Gilkeson, Robert and Yang, Michael and Donatelli, Christopher and Thawani, Rajat and Jacono, Frank J and Linden, Philip and Madabhushi, Anant},
doi = {10.1007/s00330-017-4813-0},
issn = {1432-1084 (Electronic)},
journal = {European radiology},
keywords = {Adenocarcinoma,Adult,Aged,Aged, 80 and over,Female,Humans,Lung Neoplasms,Male,Middle Aged,Multiple Pulmonary Nodules,Neoplasm Invasiveness,Proof of Concept Study,Tomography, X-Ray Computed,diagnostic imaging,methods,pathology},
language = {eng},
mendeley-groups = {MIDL2020},
month = {oct},
number = {10},
pages = {4209--4217},
pmid = {28386717},
title = {{Co-registration of pre-operative CT with ex vivo surgically excised ground glass  nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study.}},
volume = {27},
year = {2017}
}


@article{Nameeta2016,
author = {Nameeta, Shah and Feng, Xu and {Lankerovich, Michael Puchalski}, Ralph B. and Keogh, Bart},
doi = {10.7937/K9/TCIA.2016.XLwaN6nL},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Data from Ivy GAP}},
year = {2016}
}

@article{Grove2015,
abstract = {Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC{\textgreater}/=0.897; dynamic range, DR{\textgreater}/=0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.},
author = {Grove, Olya and Berglund, Anders E and Schabath, Matthew B and Aerts, Hugo J W L and Dekker, Andre and Wang, Hua and Velazquez, Emmanuel Rios and Lambin, Philippe and Gu, Yuhua and Balagurunathan, Yoganand and Eikman, Edward and Gatenby, Robert A and Eschrich, Steven and Gillies, Robert J},
doi = {10.1371/journal.pone.0118261},
issn = {1932-6203 (Electronic)},
journal = {PloS one},
keywords = {Adenocarcinoma,Adenocarcinoma of Lung,Aged,Entropy,Female,Humans,Image Processing, Computer-Assisted,Lung Neoplasms,Male,Middle Aged,Prognosis,Proportional Hazards Models,Retrospective Studies,Tomography, X-Ray Computed,diagnostic imaging,pathology},
language = {eng},
mendeley-groups = {MIDL2020},
number = {3},
pages = {e0118261},
pmid = {25739030},
title = {{Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma.}},
volume = {10},
year = {2015}
}


@article{Erickson2017,
author = {Erickson, Bradley and Akkus, Zeynettin and Sedlar, Jiri and Korfiatis, Panagiotis},
doi = {10.7937/K9/TCIA.2017.dwehtz9v},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Data From LGG-1p19qDeletion}},
year = {2017}
}

@article{Newitt2016a,
author = {Newitt, David and Nola, Hylton},
doi = {10.7937/K9/TCIA.2016.HdHpgJLK},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials.}},
year = {2016}
}


@article{Bosch2015,
author = {Bosch, Walter R. and Straube, William L. and Matthews, John W. and Purdy, James A.},
doi = {10.7937/K9/TCIA.2015.7AKGJUPZ},
mendeley-groups = {MIDL2020},
title = {{Data From Head-Neck{\_}Cetuximab}},
year = {2015}
}

@article{Bloch2015,
author = {Bloch, B. Nicolas and Jain, Ashali and Jaffe, C. Carl.},
doi = {10.7937/K9/TCIA.2015.SDNRQXXR},
journal = {The Cancer Imaging Archive},
title = {{Data From BREAST-DIAGNOSIS}},
year = {2015}
}
@article{Gonzalez2018,
abstract = {RATIONALE: Deep learning is a powerful tool that may allow for improved outcome prediction. OBJECTIVES: To determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease (ARD) events and mortality in smokers. METHODS: A CNN was trained using computed tomography scans from 7,983 COPDGene participants and evaluated using 1,000 nonoverlapping COPDGene participants and 1,672 ECLIPSE participants. Logistic regression (C statistic and the Hosmer-Lemeshow test) was used to assess COPD diagnosis and ARD prediction. Cox regression (C index and the Greenwood-Nam-D'Agnostino test) was used to assess mortality. MEASUREMENTS AND MAIN RESULTS: In COPDGene, the C statistic for the detection of COPD was 0.856. A total of 51.1{\%} of participants in COPDGene were accurately staged and 74.95{\%} were within one stage. In ECLIPSE, 29.4{\%} were accurately staged and 74.6{\%} were within one stage. In COPDGene and ECLIPSE, the C statistics for ARD events were 0.64 and 0.55, respectively, and the Hosmer-Lemeshow P values were 0.502 and 0.380, respectively, suggesting no evidence of poor calibration. In COPDGene and ECLIPSE, CNN predicted mortality with fair discrimination (C indices, 0.72 and 0.60, respectively), and without evidence of poor calibration (Greenwood-Nam-D'Agnostino P values, 0.307 and 0.331, respectively). CONCLUSIONS: A deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.},
author = {Gonzalez, German and Ash, Samuel Y and Vegas-Sanchez-Ferrero, Gonzalo and {Onieva Onieva}, Jorge and Rahaghi, Farbod N and Ross, James C and Diaz, Alejandro and {San Jose Estepar}, Raul and Washko, George R},
doi = {10.1164/rccm.201705-0860OC},
institution = {COPDGene and ECLIPSE Investigators},
issn = {1535-4970 (Electronic)},
journal = {American journal of respiratory and critical care medicine},
keywords = {Aged,Databases, Factual,Deep Learning,Female,Humans,Logistic Models,Male,Middle Aged,Predictive Value of Tests,Prognosis,Pulmonary Disease, Chronic Obstructive,Respiratory Distress Syndrome, Adult,Respiratory Function Tests,Risk Assessment,Severity of Illness Index,Smoking,Survival Rate,Tomography, X-Ray Computed,adverse effects,diagnostic imaging,epidemiology,genetics,methods},
language = {eng},
month = {jan},
number = {2},
pages = {193--203},
pmid = {28892454},
title = {{Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography.}},
volume = {197},
year = {2018}
}

@article{Smith2015,
author = {Smith, K and Clark, K and Bennett, W and Nolan, T and Kirby, J and Wolfsberger, M and Moulton, J and Vendt, B and Freymann, J},
doi = {10.7937/K9/TCIA.2015.NWTESAY1},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Data From CT{\_}COLONOGRAPHY}},
year = {2015}
}


@inproceedings{Deng2009,
author = {Deng, J and Dong, W and Socher, R and Li, L and Li, Kai and Fei-Fei, Li},
booktitle = {2009 IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2009.5206848},
isbn = {1063-6919 VO  -},
keywords = {Explosions,Image databases,Image retrieval,ImageNet database,Information retrieval,Internet,Large-scale systems,Multimedia databases,Ontologies,Robustness,Spine,computer vision,image resolution,image retrieval,large-scale hierarchical image database,large-scale ontology,multimedia computing,multimedia data,ontologies (artificial intelligence),subtree,trees (mathematics),very large databases,visual databases,wordNet structure},
pages = {248--255},
title = {{ImageNet: A large-scale hierarchical image database}},
year = {2009}
}
@article{Song2018,
abstract = {BACKGROUND: Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). PURPOSE: To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. STUDY TYPE: Retrospective. SUBJECTS: In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. SEQUENCE: T2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. ASSESSMENT: A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. STATISTICAL TEST: Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. RESULTS: The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95{\%} confidence interval: 0.876-0.994), sensitivity of 87.0{\%}, specificity of 90.6{\%}, PPV of 87.0{\%}, and NPV of 90.6{\%}. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. DATA CONCLUSION: The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer-aided diagnosis (CAD) for PCa classification. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1570-1577.},
author = {Song, Yang and Zhang, Yu-Dong and Yan, Xu and Liu, Hui and Zhou, Minxiong and Hu, Bingwen and Yang, Guang},
doi = {10.1002/jmri.26047},
issn = {1522-2586 (Electronic)},
journal = {Journal of magnetic resonance imaging : JMRI},
keywords = {Area Under Curve,Databases, Factual,Deep Learning,Diagnosis, Computer-Assisted,Diffusion Magnetic Resonance Imaging,Humans,Male,Neural Networks, Computer,Pattern Recognition, Automated,Predictive Value of Tests,Prostatic Neoplasms,ROC Curve,Reproducibility of Results,Retrospective Studies,Sensitivity and Specificity,Software,diagnostic imaging,methods},
language = {eng},
month = {dec},
number = {6},
pages = {1570--1577},
pmid = {29659067},
title = {{Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.}},
volume = {48},
year = {2018}
}
@article{Patnana2019,
author = {Patnana, M. and Patel, S. and Tsao, A.},
doi = {10.7937/tcia.2019.1ae0qtcu},
journal = {The Cancer Imaging Archive},
title = {{Anti-PD-1 Immunotherapy Melanoma Dataset [Data set]}},
year = {2019}
}

@article{CPTAC-UCEC,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/k9/tcia.2018.3r3juisw},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma [CPTAC-UCEC] Collection [Data set]}},
year = {2018}
}


@article{Cardenas2019,
author = {Cardenas, Carlos E and Mohamed, A and Sharp, G. and Gooding, M. and Veeraraghavan, H. and Yang, J.},
doi = {10.7937/tcia.2019.bcfjqfqb},
journal = {The Cancer Imaging Archive},
title = {{Data from AAPM RT-MAC Grand Challenge 2019}},
year = {2019}
}
@article{Heller2019,
author = {Heller, N. and Sathianathen, N. and {Kalapara, A., Walczak}, E. and Moore, K. and Kaluzniak, H. and Rosenberg, J. and Blake, P. and Rengel, Z. and Oestreich, M. and Dean, J. and Tradewell, M. and Shah, A. and Tejpaul, R. and Edgerton, Z. and Peterson, M. and Raza, S. and Regmi, S. and Papanikolopoulos, N. and Weight, C.},
doi = {10.7937/TCIA.2019.IX49E8NX},
journal = {The Cancer Imaging Archive},
title = {{Data from C4KC-KiTS [Data set]}},
year = {2019}
}
@article{CPTAC-CCRCC,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/k9/tcia.2018.oblamn27},
journal = {The Cancer Imaging Archive},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma [CPTAC-CCRCC] collection [Data set]}},
year = {2018}
}
@article{tencent2019,
archivePrefix = {arXiv},
arxivId = {1904.00625},
author = {Chen, Sihong and Ma, Kai and Zheng, Yefeng},
eprint = {1904.00625},
journal = {CoRR},
title = {{Med3D: Transfer Learning for 3D Medical Image Analysis}},
url = {http://arxiv.org/abs/1904.00625},
volume = {abs/1904.00625},
year = {2019}
}
@article{Schmainda2018,
author = {Schmainda, K.M. and Prah, M},
doi = {10.7937/K9/TCIA.2018.15quzvnb},
journal = {The Cancer Imaging Archive},
title = {{Data from Brain-Tumor-Progression}},
year = {2018}
}
@article{Newitt2016,
author = {Newitt, David and Hylton, Hylton},
doi = {10.7937/K9/TCIA.2016.QHsyhJKy},
journal = {The Cancer Imaging Archive},
title = {{Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy}},
year = {2016}
}
@article{CPTAC-GBM,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/k9/tcia.2018.3rje41q1},
journal = {The Cancer Imaging Archive},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme [CPTAC-GBM] collection [Data set]. The Cancer Imaging Archive.}},
year = {2018}
}
@article{CPTAC-HNSCC,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/K9/TCIA.2018.UW45NH81},
journal = {The Cancer Imaging Archive},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma [CPTAC-HNSCC] Collection}},
year = {2018}
}
@article{CPTAC-LUAD,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/k9/tcia.2018.pat12tbs},
journal = {The Cancer Imaging Archive},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma [CPTAC-LUAD] collection [Data set]}},
year = {2018}
}
@article{CPTAC-PDA,
author = {CPTAC, National Cancer Institute Clinical Proteomic Tumor Analysis Consortium},
doi = {10.7937/k9/tcia.2018.sc20fo18},
journal = {The Cancer Imaging Archive},
title = {{Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma [CPTAC-PDA] Collection [Data set]}},
year = {2018}
}

@article{Kirk2016d,
author = {Kirk, S. and Lee, Y. and Sadow, C. A. and Levine, S.},
doi = {10.7937/K9/TCIA.2016.F7PPNPNU},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Rectum Adenocarcinoma [TCGA-READ] collection}},
year = {2016}
}

@article{Roche2016a,
author = {Roche, C. and Bonaccio, E. and Filippini, J.},
doi = {10.7937/K9/TCIA.2016.CX6YLSUX},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Sarcoma [TCGA-SARC] collection}},
year = {2016}
}

@article{Kirk2016e,
author = {Kirk, S. and Lee, Y. and Roche, C. and Bonaccio, E. and Filippini, J. and Jarosz, R.},
doi = {10.7937/K9/TCIA.2016.9ZFRVF1B},
journal = {The Cancer Imaging Archive},
mendeley-groups = {MIDL2020},
title = {{Radiology Data from The Cancer Genome Atlas Thyroid Cancer [TCGA-THCA] collection}},
year = {2016}
}

