@article{Siegel2023,
  title={Cancer statistics, 2023},
  author={Siegel, Rebecca L. and Miller, Kimberly D. and Jemal, Ahmedin},
  journal={CA: A Cancer Journal for Clinicians},
  year={2023}
}

@article{Katz2013,
  title={Borderline resectable pancreatic cancer: need for standardization and methods for optimal clinical trial design},
  author={Katz, Matthew H.G. and Pisters, Peter W.T. and Evans, D. et al.},
  journal={Annals of Surgical Oncology},
  year={2013}
}

@article{Hosny2018,
  title={Artificial intelligence in radiology},
  author={Hosny, Ahmed and Parmar, Chintan and Quackenbush, John and Schwartz, Lawrence H. and Aerts, Hugo JWL},
  journal={Nature Reviews Cancer},
  year={2018}
}

@inproceedings{Wei2020,
  title={Multi-task learning with dynamic loss balancing for medical image analysis},
  author={Wei, Xiang and others},
  booktitle={CVPR Workshops},
  year={2020}
}

@article{Giannone2021,
  title = {Resectability of Pancreatic Cancer Is in the Eye of the Observer: A Multicenter,  Blinded,  Prospective Assessment of Interobserver Agreement on NCCN Resectability Status Criteria},
  volume = {2},
  ISSN = {2691-3593},
  url = {http://dx.doi.org/10.1097/AS9.0000000000000087},
  DOI = {10.1097/as9.0000000000000087},
  number = {3},
  journal = {Annals of Surgery Open},
  publisher = {Ovid Technologies (Wolters Kluwer Health)},
  author={Giannone, Fabio and Capretti, Giovanni and Hilal, Mohammed Abu and Boggi, Ugo and Campra, Donata and Cappelli, Carla and Casadei, Riccardo and De Luca, Raffaele and Falconi, Massimo and Giannotti, Gabriele and others},
  year = {2021},
  month = aug,
  pages = {e087}
}




@article{Nasief2019,
  title = {A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer},
  volume = {3},
  ISSN = {2397-768X},
  url = {http://dx.doi.org/10.1038/s41698-019-0096-z},
  DOI = {10.1038/s41698-019-0096-z},
  number = {1},
  journal = {npj Precision Oncology},
  publisher = {Springer Science and Business Media LLC},
  author = {Nasief,  Haidy and Zheng,  Cheng and Schott,  Diane and Hall,  William and Tsai,  Susan and Erickson,  Beth and Allen Li,  X.},
  year = {2019},
  month = oct 
}

@article{Huang2021,
  title = {Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis},
  volume = {25},
  ISSN = {2168-2208},
  url = {http://dx.doi.org/10.1109/JBHI.2020.3043236},
  DOI = {10.1109/jbhi.2020.3043236},
  number = {7},
  journal = {IEEE Journal of Biomedical and Health Informatics},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Huang,  Bingsheng and others},
  year = {2021},
  month = jul,
  pages = {2655–2664}
}


@article{Attiyeh2018,
  title = {Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis},
  volume = {25},
  ISSN = {1534-4681},
  url = {http://dx.doi.org/10.1245/s10434-017-6323-3},
  DOI = {10.1245/s10434-017-6323-3},
  number = {4},
  journal = {Annals of Surgical Oncology},
  publisher = {Springer Science and Business Media LLC},
  author = {Attiyeh,  Marc A. and others},
  year = {2018},
  month = jan,
  pages = {1034–1042}
}



@misc{dong2023improvedprognosticpredictionpancreatic,
      title={Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer}, 
      author={Hexin Dong and Jiawen Yao and Yuxing Tang and Mingze Yuan and Yingda Xia and Jian Zhou and Hong Lu and Jingren Zhou and Bin Dong and Le Lu and Li Zhang and Zaiyi Liu and Yu Shi and Ling Zhang},
      year={2023},
      eprint={2308.00507},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2308.00507}, 
}

@misc{cao2021swinunetunetlikepuretransformer,
      title={Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation}, 
      author={Hu Cao and others},
      year={2021},
      eprint={2105.05537},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2105.05537}, 
}

@article{vanGriethuysen2017,
  title = {Computational Radiomics System to Decode the Radiographic Phenotype},
  volume = {77},
  ISSN = {1538-7445},
  url = {http://dx.doi.org/10.1158/0008-5472.CAN-17-0339},
  DOI = {10.1158/0008-5472.can-17-0339},
  number = {21},
  journal = {Cancer Research},
  publisher = {American Association for Cancer Research (AACR)},
  author = {van Griethuysen,  Joost J.M. and others},
  year = {2017},
  month = oct,
  pages = {e104–e107}
}

@article{Tibshirani1996,
  title = {Regression Shrinkage and Selection Via the Lasso},
  volume = {58},
  ISSN = {1467-9868},
  url = {http://dx.doi.org/10.1111/j.2517-6161.1996.tb02080.x},
  DOI = {10.1111/j.2517-6161.1996.tb02080.x},
  number = {1},
  journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
  publisher = {Oxford University Press (OUP)},
  author = {Tibshirani,  Robert},
  year = {1996},
  month = jan,
  pages = {267–288}
}

@InProceedings{10.1007/978-3-319-24574-4_28,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
isbn="978-3-319-24574-4"
}

@article{Chatterjee_2022,
   title={DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data},
   volume={8},
   ISSN={2313-433X},
   url={http://dx.doi.org/10.3390/jimaging8100259},
   DOI={10.3390/jimaging8100259},
   number={10},
   journal={Journal of Imaging},
   publisher={MDPI AG},
   author={Chatterjee, Soumick and others},
   year={2022},
   month=sep, pages={259} }


@misc{cardoso2022monaiopensourceframeworkdeep,
      title={MONAI: An open-source framework for deep learning in healthcare}, 
      author={M. Jorge Cardoso and others},
      year={2022},
      eprint={2211.02701},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2211.02701}, 
}

@misc{yadav2017miningelectronichealthrecords,
      title={Mining Electronic Health Records: A Survey}, 
      author={Pranjul Yadav and Michael Steinbach and Vipin Kumar and Gyorgy Simon},
      year={2017},
      eprint={1702.03222},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/1702.03222}, 
}

@misc{jiang2021fusionmedicalimagingelectronic,
      title={Fusion of medical imaging and electronic health records with attention and multi-head machanisms}, 
      author={Cheng Jiang and Yihao Chen and Jianbo Chang and Ming Feng and Renzhi Wang and Jianhua Yao},
      year={2021},
      eprint={2112.11710},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2112.11710}, 
}

@article{Huang2016,
  title = {Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer},
  volume = {281},
  ISSN = {1527-1315},
  url = {http://dx.doi.org/10.1148/radiol.2016152234},
  DOI = {10.1148/radiol.2016152234},
  number = {3},
  journal = {Radiology},
  publisher = {Radiological Society of North America (RSNA)},
  author = {Huang,  Yanqi and others},
  year = {2016},
  month = dec,
  pages = {947–957}
}

@misc{peng2020multimodalityinformationfusionradiomicsbased,
      title={Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search}, 
      author={Yige Peng and Lei Bi and Michael Fulham and Dagan Feng and Jinman Kim},
      year={2020},
      eprint={2007.06002},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2007.06002}, 
}



@misc{dai2023radiomicsinformeddeeplearningclassification,
      title={Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes}, 
      author={Weihang Dai and Xiaomeng Li and Taihui Yu and Di Zhao and Jun Shen and Kwang-Ting Cheng},
      year={2023},
      eprint={2308.06933},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2308.06933}, 
}



@misc{saeed2022tmssendtoendtransformerbasedmultimodal,
      title={TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction}, 
      author={Numan Saeed and Ikboljon Sobirov and Roba Al Majzoub and Mohammad Yaqub},
      year={2022},
      eprint={2209.05036},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2209.05036}, 
}


@Article{jcm12216821,
AUTHOR = {Khasawneh, Hala and others},
TITLE = {CT Imaging Assessment of Pancreatic Adenocarcinoma Resectability after Neoadjuvant Therapy: Current Status and Perspective on the Use of Radiomics},
JOURNAL = {Journal of Clinical Medicine},
VOLUME = {12},
YEAR = {2023},
NUMBER = {21},
ARTICLE-NUMBER = {6821},
URL = {https://www.mdpi.com/2077-0383/12/21/6821},
PubMedID = {37959287},
ISSN = {2077-0383},
ABSTRACT = {Pancreatic adenocarcinoma (PDAC) is the most common pancreatic cancer and is associated with poor prognosis, a high mortality rate, and a substantial number of healthy life years lost. Surgical resection is the primary treatment option for patients with resectable disease; however, only 10–20% of all patients with PDAC are eligible for resection at the time of diagnosis. In this context, neoadjuvant therapy has the potential to increase the number of patients who are eligible for resection, thereby improving the overall survival rate. For patients who undergo neoadjuvant therapy, computed tomography (CT) remains the primary imaging tool for assessing treatment response. Nevertheless, the interpretation of imaging findings in this context remains challenging, given the similarity between viable tumor and treatment-related changes following neoadjuvant therapy. In this review, following an overview of the various treatment options for PDAC according to its resectability status, we will describe the key challenges regarding CT-based evaluation of PDAC treatment response following neoadjuvant therapy, as well as summarize the literature on CT-based evaluation of PDAC treatment response, including the use of radiomics. Finally, we will outline key recommendations for the management of PDAC after neoadjuvant therapy, taking into consideration CT-based findings.},
DOI = {10.3390/jcm12216821}
}



@Article{diagnostics13203198,
AUTHOR = {Litjens, Geke and others},
TITLE = {Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head},
JOURNAL = {Diagnostics},
VOLUME = {13},
YEAR = {2023},
NUMBER = {20},
ARTICLE-NUMBER = {3198},
URL = {https://www.mdpi.com/2075-4418/13/20/3198},
PubMedID = {37892019},
ISSN = {2075-4418},
ABSTRACT = {The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team’s (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT’s prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.},
DOI = {10.3390/diagnostics13203198}
}



@misc{yao2020deepprognosispreoperativepredictionpancreatic,
      title={DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging}, 
      author={Jiawen Yao and others},
      year={2020},
      eprint={2008.11853},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2008.11853}, 
}





@article{Sung2021,
  title={Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
  author={Sung, Hyuna and Ferlay, Jacques and Siegel, Rebecca L and Laversanne, Mathieu and Soerjomataram, Isabelle and Jemal, Ahmedin and Bray, Freddie},
  journal={CA: A Cancer Journal for Clinicians},
  volume={71},
  number={3},
  pages={209--249},
  year={2021},
  publisher={Wiley Online Library},
  doi={10.3322/caac.21660}
}

@article{Bereska2024,
  title = {Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer},
  volume = {8},
  ISSN = {2509-9280},
  url = {http://dx.doi.org/10.1186/s41747-023-00419-9},
  DOI = {10.1186/s41747-023-00419-9},
  number = {1},
  journal = {European Radiology Experimental},
  publisher = {Springer Science and Business Media LLC},
  author = {Bereska,  Jacqueline I. and Janssen,  Boris V. and Nio,  C. Yung and Kop,  Marnix P. M. and Kazemier,  Geert and Busch,  Olivier R. and Struik,  Femke and Marquering,  Henk A. and Stoker,  Jaap and Besselink,  Marc G. and Verpalen,  Inez M.},
  year = {2024},
  month = feb 
}


@misc{viviers2023,
      title={Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma}, 
      author={Christiaan Viviers and Mark Ramaekers and Amaan Valiuddin and Terese Hellström and Nick Tasios and John van der Ven and Igor Jacobs and Lotte Ewals and Joost Nederend and Peter de With and Misha Luyer and Fons van der Sommen},
      year={2023},
      eprint={2310.00639},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2310.00639}, 
}




@misc{Ronneberger2015,
      title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
      author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
      year={2015},
      eprint={1505.04597},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1505.04597}, 
}

@article{Rawla2019,
  title={Epidemiology of pancreatic cancer: Global trends, etiology and risk factors},
  author={Rawla, Prashanth and Sunkara, Thandra and Gaduputi, Vinaya},
  journal={World Journal of Oncology},
  year={2019},
  volume={10},
  pages={10--27}
}

@article{Tempero2021,
  title={Pancreatic adenocarcinoma, version 2.2021, NCCN clinical practice guidelines in oncology},
  author={Tempero, Margaret A and others},
  journal={Journal of the National Comprehensive Cancer Network},
  year={2021},
  volume={19},
  number={4},
  pages={439--457}
}

@article{Neoptolemos2018,
  title={Therapeutic developments in pancreatic cancer: Current and future perspectives},
  author={Neoptolemos, John P and others},
  journal={Nature Reviews Gastroenterology \& Hepatology},
  year={2018},
  volume={15},
  pages={333--348}
}

@article{Bockhorn2014,
  title={Borderline resectable pancreatic cancer: a consensus statement by the International Study Group of Pancreatic Surgery (ISGPS)},
  author={Bockhorn, M. and others},
  journal={Surgery},
  year={2014},
  volume={155},
  number={6},
  pages={977--988}
}

@article{Isaji2018,
  title={International consensus on definition and criteria of borderline resectable pancreatic ductal adenocarcinoma 2017},
  author={Isaji, S. and others},
  journal={Pancreatology},
  year={2018},
  volume={18},
  pages={2--11}
}

@article{Callery2009,
  title={Pretreatment assessment of resectable and borderline resectable pancreatic cancer: expert consensus statement},
  author={Callery, M. P. and others},
  journal={Annals of Surgical Oncology},
  year={2009},
  volume={16},
  pages={1727--1733}
}

@article{Litjens2017,
  title     = {A survey on deep learning in medical image analysis},
  author    = {Geert Litjens and others},
  journal   = {Medical Image Analysis},
  year      = {2017},
  volume    = {42},
  pages     = {60--88},
  doi       = {10.1016/j.media.2017.07.005}
}

@article{Li,
  title={Medical image analysis using deep learning algorithms},
  author={Li, M. and others},
  journal={Front Public Health},
  year={2023}
}


@article{Zhou2020,
  title={Automatic segmentation of pancreas in abdominal CT images using deep convolutional neural network and self-supervised learning},
  author={Zhou, Y. and others},
  journal={Medical Physics},
  year={2020},
  volume={47},
  pages={5431--5442}
}

@article{ZOU2024102825,
title = {Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism},
journal = {Artificial Intelligence in Medicine},
volume = {150},
pages = {102825},
year = {2024},
issn = {0933-3657},
doi = {https://doi.org/10.1016/j.artmed.2024.102825},
url = {https://www.sciencedirect.com/science/article/pii/S0933365724000678},
author = {Liwen Zou and Zhenghua Cai and Liang Mao and Ziwei Nie and Yudong Qiu and Xiaoping Yang}
}

@article{Huang,
  title={Artificial intelligence in pancreatic cancer.},
  author={Huang, B. and Huang, H. and Zhang, S. and Zhang,  D. and  Shi, Q and  Liu,  J and Guo J.},
  journal={Theranostics},
  year={2022},
  volume={12},
  pages={6931-6954}
}

.

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  title={Pancreatic cancer surgery in the new millennium: better prediction of outcome.},
  author={Hartwig, Wolfgang and others},
  journal={Annals of Surgery},
  year={2011},
  volume={254}
}


@article{Rahib2014,
  title={Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States.},
  author={Rahib, Lola and others},
  journal={Cancer Research},
  year={2014},
  volume={74},
  number={11},
  pages={2913--2921}
}



@article{Lim,
  title     = {Automated pancreas segmentation and volumetry using deep learning: a large N study},
  author    = {S. H. Lim and others},
  journal   = {European Radiology},
  year      = {2022},
  volume    = {32},
  number    = {6},
  pages     = {4563–4572},
  doi       = {10.1007/s00330-022-08653-w}
}



@article{Schuurmans2025_EndToEndPDAC,
  title   = { End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study},
  author  = {M. Schuurmans and others},
  journal = {European Radiology},
  year    = {2025},
  note    = {Multimodal AI for PDAC prognostic stratification}
}

@misc{ji2022amoslargescaleabdominalmultiorgan,
      title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, 
      author={Yuanfeng Ji and Haotian Bai and Jie Yang and Chongjian Ge and Ye Zhu and Ruimao Zhang and Zhen Li and Lingyan Zhang and Wanling Ma and Xiang Wan and Ping Luo},
      year={2022},
      eprint={2206.08023},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2206.08023}, 
}


@article{KORDNOORI2024101931,
title = {Deep multi-task learning structure for segmentation and classification of supratentorial brain tumors in MR images},
journal = {Interdisciplinary Neurosurgery},
volume = {36},
pages = {101931},
year = {2024},
issn = {2214-7519},
doi = {https://doi.org/10.1016/j.inat.2023.101931},
url = {https://www.sciencedirect.com/science/article/pii/S2214751923002141},
author = {Shirin Kordnoori and Maliheh Sabeti and Mohammad Hossein Shakoor and Ehsan Moradi},
keywords = {Brain tumor, Multi-task deep learning, Segmentation, Classification}
}

@inproceedings{AdamW,
  title={Decoupled Weight Decay Regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  booktitle={International Conference on Learning Representations},
  year={2019}
}
