Радиомика и искусственный интеллект в дифференциальной диагностике опухолевых и неопухолевых заболеваний поджелудочной железы (обзор)

Радиомика и искусственный интеллект в дифференциальной диагностике опухолевых и неопухолевых заболеваний поджелудочной железы (обзор)

Автор: Парамзин Ф.Н., Какоткин В.В., Буркин Д.А., Агапов М.A.

Журнал: Хирургическая практика @spractice

Рубрика: Хирургия

Статья в выпуске: 1 т.8, 2023 года.

Бесплатный доступ

Работа основана на анализе данных литературы, посвященной внедрению радиомического анализа и искусственного интеллекта (ИИ) в диагностику заболеваний поджелудочной железы, за последние 5 лет. Главная цель обзора - определить наиболее перспективные методы радиомной диагностики и возможности применения искусственного интеллекта в диагностике заболеваний поджелудочной железы. Рассмотрены основные понятия радиомики, этапы радиомического анализа (сбор данных, предварительная обработка, сегментация опухоли, обнаружение и извлечение данных, моделирование, статистическая обработка, валидация данных), оценены возможности искусственного интеллекта и искусственных нейронных сетей в хирургической и онкологической панкреатологии. Описаны особенности и преимущества применения радиомического анализа и ИИ при диагностике и прогнозировании онкологических заболеваний поджелудочной железы. Отмечены ограничения, связанные с использованием радиомики и ИИ в панкреатологии.

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Радиомика, опухоли поджелудочной железы, протоковая аденокарцинома, искусственный интеллект, количественный анализ цифровых изображений, анализ цифровых изображений в онкологии, нейронные сети

Короткий адрес: https://sciup.org/142238141

IDR: 142238141   |   DOI: 10.38181/2223-2427-2023-1-5

Список литературы Радиомика и искусственный интеллект в дифференциальной диагностике опухолевых и неопухолевых заболеваний поджелудочной железы (обзор)

  • Parr E, Du Q, Zhang C, Lin C, Kamal A, McAlister J, Liang X, Bavitz K, Rux G, Hollingsworth M, Baine M, Zheng D. Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy. Cancers. 2020. Apr 24;12(4):1051. https://doi.org/10.3390/cancers12041051.
  • Ren S, Zhao R, Zhang J, Guo K, Gu X, Duan S, Wang Z, Chen R. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdominal Radiology (NY). 2020. May 45(5):1524—1533. https://doi.org/10.1007/s00261-020-02506-6.
  • Larue RTHM, Van Timmeren JE, De Jong EEC, Feliciani G, Leijenaar RTH, Schreurs WMJ, Sosef MN, Raat FHPJ, Van der Zande FHR, Das M, Van Elmp W, Lambin P. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncologica. 2017. Nov 56(11):1544—1553. https://doi.org/10.1080/0284186X.2017.1351624.
  • Ozaki K, Ikeno H, Kaizaki Y, Maeda K, Higuchi S, Kosaka N, Kimura H, Gabata T. Pearls and pitfalls of imaging features of pancreatic cystic lesions: a case-based approach with imaging-pathologic correlation. Japanese Journal of Radiology. 2021. Feb 39(2):118—142. https://doi.org/10.1007/s11604-020-01032-1.
  • Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016. Feb 278(2):563—577. https://doi.org/10.1148/radiol.2015151169.
  • Dinapoli N, Alitto AR, Vallati M, Gatta R, Autorino R, Boldrini L, Damiani A, Valentini V. Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015. Aug 2015:771—774. https://doi.org/10.1109/EMBC.2015.7318476.
  • Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR., Davatzikos C., Depeursinge A, Desseroit M-C, Dinapoli N, Dinh CV, Echegaray S, Naqa IE, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegаrd A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Fernandez JS, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, Van Dijk LV, Van Griethuysen J, Van Velden FHP, Whybra P, Richter C, Löck S. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020. May 295(2):328—338. https://doi.org/10.1148/radiol.2020191145.
  • Yann C, Carey S, Sebastien B, Vachier I, Marin G, Bourdin A, Molinari N. k-Nearest Neighbor Curves in Imaging Data Classification. Frontiers in Applied Mathematics and Statistics. 2019;5:22. https://doi.org/10.3389/fams.2019.00022.
  • Lambin P, Leijenaar RT, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, Van Wijk Y, Woodruff H, Van Soest J, Lustberg T, Roelofs E, Van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. 2017. Dec 14(12):749—762. https://doi.org/10.1038/nrclinonc.2017.141.
  • Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, Khalvati F, Haider MA. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma — a quantitative analysis. BMC Medical Imaging. 2017. Jun 19;17(1):38. https://doi:10.1186/s12880-017-0209-5.
  • Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. 2014;5:4006. https://doi: 10.1038/ncomms5006.
  • Napel S, Mu V, Jardim-Perassi BV, Aerts HJWL, Gillies RJ. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer. 2018. Dec 15;124(24):4633—4649. https://doi.org/10.1002/cncr.31630.
  • Neri E, Del Re M, Payar F, Erba P, Cocuzza P, Regge D, Danesi R. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights into Imaging. 2018;9:915—924. https://doi.org/10.1007/s13244-018-0657-7.
  • Liu Z, Wang S, Dong D, Wei1 J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019. Feb 12;9(5):1303—1322. https://doi.org/10.7150/thno.30309.
  • Rogers W, Seetha ST, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Damiënne M, Van der Wiel AMA, Zerka F, Oberije CJG, Van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. The British Journal of Radiology. 2020. Mar 93(1108):20190948. https://doi.org/10.1259/bjr.20190948.
  • Van Grithuisen JJM, Fedorov A, Parmar С, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts HJWL. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Research. 2017. Nov 1;77(21):e104—e107. https://doi.org/10.1158/0008-5472.CAN-17-0339.
  • Park S, Sham JG, Kawamoto S, Blair AB, Rozich N, Fouladi DF, Shayesteh S, Hruban RH, He J, Wolfgang CL, Yuille AL, Fishman EK, Chu LC. CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma. American Journal of Roentgenology. 2021. Nov 217(5):1104—1112. https://doi.org/10.2214/AJR.20.23490.
  • Sohal DPS, Duong M, Ahmad SA, Gandhi NS, Beg MS, Wang-Gillam A, Wade III JL, Chiorean EG, Guthrie KA, Lowy AM, Philip PA, Hochster HS. Efficacy of Perioperative Chemotherapy for Resectable Pancreatic Adenocarcinoma: A Phase 2 Randomized Clinical Trial. JAMA Oncology. 2021. Mar 1;7(3):421—427. https://doi.org/10.1001/jamaoncol.2020.7328.
  • Limkin EJ, Sun R, Derkle L, Zacharaki EI, Robert C, Reuze S, Schernberg A, Paragios N, Deutsch E, Ferte C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Annals of Oncology. 2017. Jun 1;28(6):1191—1206. https://doi.org/10.1093/annonc/mdx034.
  • Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, Van Wijk Y, Woodruff H, Van Soest J, Lustberg T, Roelofs E, Van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. 2017. Dec 14:749—762. https://doi.org/10.1038/nrclinonc.2017.141.
  • Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J.Artificial intelligence in pancreatic cancer. Theranostics. 2022;12(16):6931—6954. https://doi.org/10.7150/thno.77949.
  • Duron L, Balvay D, Vande Perre S, Bouchouicha A, Savatovsky J, Sadik J-C, Thomassin-Naggara I, Fournier L, Lecler A. Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One. 2019. Mar 7;14(3):e0213459. https://doi.org/10.1371/journal.pone.0213459.
  • Xianze W, Yuan CW, Elon C, Yi Z, Eyad I, Ashley RD. The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer. Journal of Pancreatology. 2020. Dec 3(4):173—180. https://doi.org/10.1097/JP9.0000000000000056.
  • Shi Y-J, Zhu H-T, Liu Y-L, Wei Y-Y, Qin X-B, Zhang X-Y, Li X-T, Sun Y-S. Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors. Frontiers in Oncology. 2020. Aug 21;10:1624. https://doi.org/10.3389/fonc.2020.01624.
  • Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D. Automated abdominal multi-organ segmentation with subject-specific atlas generation. Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Transactions on Medical Imaging. 2013. Sep 32:1723—1730. https://doi: 10.1109/TMI.2013.2265805.
  • Tong T, Woltz R, Van Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D. Discriminative dictionary learning for abdominal multi-organ segmentation. Medical Image Analysis. 2015. Jul 23(1):92—104. https://doi.org/10.1016/j.media.2015.04.015.
  • Li J, Lu J, Liang P, Li А, Hu Y, Shen Y, Hu D, Li Z. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Medicine. 2018. Oct 7 (10):4924—4931. https://doi.org/10.1002/cam4.1746.
  • Aslan S, Nural MS, Camlidag I, Danaci M. Efficacy of perfusion CT in differentiating of pancreatic ductal adenocarcinoma from mass-forming chronic pancreatitis and characterization of isoattenuating pancreatic lesions. Abdominal Radiology (NY). 2019;44:593—603. https://doi.org/10.1007/s00261-018-1776-9.
  • Ruan Z, Jiao J, Min D, Qu J, Li J, Chen J, Li Q, Wang C. Multi-modality imaging features distinguish pancreatic carcinoma from mass-forming chronic pancreatitis of the pancreatic head. Oncology Letters. 2018;15:9735—9744. https://doi.org/10.3892/ol.2018.8545.
  • Yadav AK, Sharma R, Kandasamy D, Pradhan RK, Garg PK, Bhalla AS, Gamanagatti S, Srivastava DN, Sahni P, Upadhyay AD. Perfusion CT-Can it resolve the pancreatic carcinoma versus mass forming chronic pancreatitis conundrum? Pancreatology. 2016;16:979—987. https://doi.org/10.1016/j.pan.2016.08.011.
  • Frampas E, Morla O, Regenet N, Eugene T, Dupas B, Meurette G. A solid pancreatic mass: tumour or inflammation? Diagnostic and Interventional Imaging. 2013. Jul-Aug 94(7-8):741—755. https://doi.org/10.1016/j.diii.2013.03.013.
  • Ding Y, Zhou J, Sun H, He D, Zeng M, Rao S. Contrast-enhanced multiphasic CT and MRI findings of adenosquamous carcinoma of the pancreas. Clinical Imaging. 2013. Nov-Dec 37(6):1054—1060. https://doi.org/10.1016/j.clinimag.2013.08.002.
  • Deshpande SS, Joshi AR, Mankar D. Pancreatic Neoplasms: CT Evaluation of the Uncommon Presentations of Common Lesions and Common Presentations of the Uncommon Lesions! Indian J Radiol Imaging. 2022 Aug 30;32(4):531—539. https://doi.org/10.1055/s-0042-1754359.
  • Tajima Y, Kuroki T, Tsutsumi R, Isomoto I, Uetani M, Kanematsu T. Pancreatic carcinoma coexisting with chronic pancreatitis versus tumor-forming pancreatitis: Diagnostic utility of the time-signal intensity curve from dynamic contrast-enhanced MR imaging. World Journal of Gastroenterology. 2007. Feb 14;13(6):858—865. https://doi.org/10.3748/wjg.v13.i6.858.
  • Ren S, Zhang J, Chen J, Cui W, Zhao R, Qiu W, Duan S, Chen R, Chen X, Wang Z. Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images. Frontiers in Oncology. 2019;9. https://doi.org/10.3389/fonc.2019.01171.
  • Yoshinaga S, Suzuki H, Oda I, Saito Y. Role of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) for diagnosis of solid pancreatic masses. Digestive Endoscopy. 2011. May 23 Suppl 1:29—33. https://doi.org/10.1111/j.1443-1661.2011.01112.x.
  • Sandrasegaran K, Nutakki K, Tahir B, Dhanabal A, Tann M, Cote GA. Use of diffusion-weighted MRI to differentiate chronic pancreatitis from pancreatic cancer. American Journal of Roentgenology. 2013. Nov 201(5):1002—1008. https://doi.org/10.2214/AJR.12.10170.
  • Majumder S, Chari ST. Chronic pancreatitis. Lancet. 2016;387:1957—1966. https://doi.org/10.1016/S0140-6736(16)00097-0.
  • Chung YE, Kim MJ, Choi JY, Seok LJ, Hye-Suk H, Chul KY, Je CH, Ah KK, Young CS. Differentiation of benign and malignant solid pseudopapillary neoplasms of the pancreas. Journal of Computer Assisted Tomography. 2009;33:689—694. https://doi.org/10.1097/RCT.0b013e31818f2a74.
  • Tong T, Gu J, Xu D, Song L, Zhao Q, Cheng F, Yuan Z, Tian S, Yang X, Tian J, Wang K, Jiang T. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis. BMC Medicine. 2022;20:74. https://doi.org/10.1186/s12916-022-02258-8.
  • He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90.
  • Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK. Utility of ct radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. American Journal of Roentgenology. 2019;213:349—357. https://doi.org/10.2214/ajr.18.20901.
  • Yun G, Kim YH, Lee YJ, Kim B, Hwang J-H, Choi DJ. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Scientific Reports. 2018;7226. https://doi.org/10.1038/s41598-018-25627-x.
  • Chen P-T, Chang D, Yen H, Liu K-L, Huang S-Y, Roth H, Wu M-S, Liao W-C, Wang W. Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas. Radiology: Imaging Cancer. 2021;3(4):e210010. https://doi.org/10.1148/rycan.2021210010.
  • Zhang Y, Cheng C, Liu Z, Wang L, Pan G, Sun G, Chang Y, Zuo C, Yang X. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in (18) F-FDG PET/CT. Medical Physics. 2019. Oct 46(10):4520—4530. https://doi.org/10.1002/mp.13733.
  • Casa C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Therapeutic Advances in Gastrointestinal Endoscopy. 2022. Mar 16;15:26317745221081596. https://doi.org/10.1177/26317745221081596.
  • Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK. Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue. American Journal of Roentgenology. 2019. Aug 213(2):349—357. https://doi.org/10.2214/AJR.18.20901.
  • Polk SL, Choi JV, McGettigan MJ, Rose T, Ahmed A, Kim J, Jiang K, Balagurunathan Y, Qi J, Farah PT, Rathi A, Permuth JB, Jeong D. Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy. World Journal of Gastroenterology. 2020. Jun 28;26(24):3458—3471. https://doi.org/10.3748/wjg.v26.i24.3458.
  • Tobaly D, Santinha J, Sartoris R, Burgio MD, Matos C, Cros J, Couvelard A, Rebours V, Sauvanet A, Ronot M, Papanikolaou N, Vilgrain V. CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas. Cancers (Basel). 2020. Oct 23;12(11):3089. https://doi.org/10.3390/cancers12113089.
  • Liu Z, Li M, Zuo C, Yang Z, Yang X, Ren S, Peng Y, Sun G, Shen J, Cheng C, Yang X. Radiomics model of dualtime 2- [18 F] FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis. European Radiology. 2021. Sep 31(9):6983—6991. https://doi.org/10.1007/s00330-021-07778-0.
  • Park S, Chu LC, Hruban RH, Vogelstein B, Kinzler KW, Yuille AL, Fouladi DF, Shayesteh S, Ghandili S, Wolfgang CL, Burkhart R, He J, Fishman EK, Kawamoto S. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagnostic and Interventional Imaging. 2020. Sep 101(9):555—564. https://doi.org/10.1016/j.diii.2020.03.002.
  • Linning E, Yan X, Zhifeng W, Li L, Na Z, Hao Y, Lawrence HS, Lin L, Binsheng Z. Differentiation of Focal- Type Autoimmune Pancreatitis From Pancreatic Ductal Adenocarcinoma Using Radiomics Based on Multiphasic Computed Tomography. Journal of Computer Assisted Tomography. 2020. Jul-Aug 44(4):511-518. https://doi.org/10.1097/RCT.0000000000001049.
  • Ziegelmayer S, Kaissis G, Harder F, Jungmann F, Müller T, Makowski M, Braren R. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP). Journal of Clinical Medicine. 2020. Dec 11;9(12):4013. https://doi.org/10.3390/jcm9124013.
  • Ren S, Zhao R, Zhang J, Guo K, Gu X, Duan S, Wang Z, Chen R. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdominal Radiology (NY). 2020. May 45(5):1524—1533. https://doi.org/10.1007/s00261-020-02506-6.
  • Yamashita R, Perrin T, Chakraborty J, Chou JF, Horvat N, Koszalka MA, Midya A, Gonen M, Allen P, Jarnagin WR, Simpson AL, Do RKG. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. European Radiology. 2020. Jan 30(1):195— 205. https://doi.org/10.1007/s00330-019-06381-8.
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