Modern Radiation Diagnostics and Intelligent Personalized Technologies in Hepatopancreatology

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Abstract

Timely instrumental diagnosis of diseases of the hepatopancreatoduodenal region, especially of an oncological nature, is the key to successful treatment, improving prognosis and improving the quality of life of patients. At the moment, the possibilities of radiation diagnostics make it possible to identify and evaluate the nature of the blood supply to the neoplasm, its prevalence, cellularity, and in the case of MRI studies with hepatospecific contrast agents, also evaluate the functional activity of liver cells. Nevertheless, the steady development of methods for treating cancer patients, in particular, chemotherapy, and a personalized approach to the choice of patient management tactics require a detailed assessment of the morphological types of certain neoplasms. The need for dynamic monitoring of the results of treatment, monitoring of accidentally detected, potentially malignant neoplasms, and the development of screening programs determine the steady increase in the number of CT and MR examinations performed annually in the world and in our country. These factors have led to the application of texture analysis or radiomics and machine learning algorithms. At the same time, such techniques as radiography, ultrasound, CT and MRI with extracellular and tissue-specific contrast enhancement, and MRI-DWI do not lose their significance. The ongoing research allows the Federal State Budgetary Institution National Medical Research Center of Surgery named after A.V. Vishnevsky of the Ministry of Health of Russia to implement the concept of preoperative non-invasive diagnosis and differential diagnosis of surgical and oncological diseases of the hepatopancreatoduodenal region and apply the knowledge gained in planning surgical treatment. Implementation of the problem of post-processor data processing of radiation diagnostics of surgical and oncological diseases of the hepatopancreatoduodenal region using radiomics and AI technologies is important and extremely relevant for modern medicine.

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About the authors

Grigory G. Kаrmаzаnovsky

A.V. Vishnevsky Medical Research Center of Surgery; Pirogov Russian National Research Medical University

Author for correspondence.
Email: karmazanovsky@ixv.ru
ORCID iD: 0000-0002-9357-0998
SPIN-code: 5964-2369
Scopus Author ID: 55944296600

MD, PhD, Professor, Academician of the RAS

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow; Moscow

Evgeny V. Kondratyev

A.V. Vishnevsky Medical Research Center of Surgery

Email: evgenykondratiev@gmail.com
ORCID iD: 0000-0001-7070-3391
SPIN-code: 2702-6526
Scopus Author ID: 55865664400

MD, PhD

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

Ivan S. Gruzdev

A.V. Vishnevsky Medical Research Center of Surgery

Email: gruzdev_van@mail.ru
ORCID iD: 0000-0003-0781-9898
SPIN-code: 3350-0832
Scopus Author ID: 57209689128

PhD Student

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

Valeriya S. Tikhonova

A.V. Vishnevsky Medical Research Center of Surgery

Email: vdovenkobc28@mail.ru
ORCID iD: 0000-0001-9782-7335
SPIN-code: 6252-5706
Scopus Author ID: 57219607436

PhD Student

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

Maria Yu. Shantarevich

A.V. Vishnevsky Medical Research Center of Surgery

Email: shantarevichm@list.ru
ORCID iD: 0000-0002-4518-4451
SPIN-code: 5652-5053
Scopus Author ID: 57206847669

PhD Student

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

Kseniia A. Zamyatina

A.V. Vishnevsky Medical Research Center of Surgery

Email: catos-zama@mail.ru
ORCID iD: 0000-0002-1643-6613
SPIN-code: 8672-4101
Scopus Author ID: 57212211885

PhD Student

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

Vladislava I. Stashkiv

A.V. Vishnevsky Medical Research Center of Surgery

Email: vladastashkiv@gmail.com
ORCID iD: 0000-0002-7349-1192
SPIN-code: 4319-6634
Scopus Author ID: 57219869096

PhD Student

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

Amiran Sh. Revishvili

A.V. Vishnevsky Medical Research Center of Surgery

Email: amirevi@mail.ru
ORCID iD: 0000-0003-1791-9163
SPIN-code: 8181-0826
Scopus Author ID: 7003940753

MD, PhD, Professor, Academician of the RAS

Russian Federation, 27, Bolshaya Serpukhovskaya str., 117997, Moscow

References

  1. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–1248. doi: https://doi.org/10.1016/j.mri.2012.06.010
  2. Choi SH, Kim SY, Park SH, et al. Diagnostic performance of CT, gadoxetate disodium-enhanced MRI, and PET/CT for the diagnosis of colorectal liver metastasis: Systematic review and meta-analysis. J Magn Reson Imaging. 2018;47(5):1237–1250. doi: https://doi.org/10.1002/jmri.25852
  3. Roberts LR, Sirlin CB, Zaiem F, et al. Imaging for the diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis. Hepatology. 2018;67(1):401–421. doi: https://doi.org/10.1002/hep.29487
  4. Andreucci M. Side effects of radiographic contrast media. Biomed Res Int. 2014;2014:872574. doi: https://doi.org/10.1155/2014/872574
  5. Maeda T, Oda M, Kito S, et al. Can the lower rate of CT- or MRI-related adverse drug reactions to contrast media due to stricter limitations on patients undergoing contrast-enhanced CT or MRI? Dentomaxillofac Radiol. 2020;49(2):20190214. doi: https://doi.org/10.1259/dmfr.20190214
  6. Гальчина Ю.С., Кармазановский Г.Г., Калинин Д.В., и др. Критерии диагностики «мягкой» поджелудочной железы и их влияние на возникновение панкреатического свища после панкреатодуоденальной резекции // Анналы хирургической гепатологии. — 2020. — Т. 25. — № 2. — С. 113–123. [Galchina YuS, Kаrmаzаnovsky GG, Kalinin DV, et al. Diagnostic criteria for a “soft” pancreas and their influence on the occurrence of pancreatic fistula after pancreatoduodenal. Annaly khirurgicheskoy gepatologii = Annals of HPB Surgery. 2020;25(2):113–123. (In Russ.)] doi: https://doi.org/10.16931/1995-5464.20202113-123
  7. Vreugdenburg TD, Ma N, Duncan JK, et al. Comparative diagnostic accuracy of hepatocyte-specific gadoxetic acid (Gd–EOB–DTPA) enhanced MR imaging and contrast enhanced CT for the detection of liver metastases: a systematic review and meta-analysis. Int J Colorectal Dis. 2016;31(11):1739–1749. doi: https://doi.org/10.1007/s00384-016-2664-9
  8. McInnes MD, Hibbert RM, Inácio JR, et al. Focal Nodular Hyperplasia and Hepatocellular Adenoma: Accuracy of Gadoxetic Acid-enhanced MR Imaging — A Systematic Review. Radiology. 2015;277(2):413–423. doi: https://doi.org/10.1148/radiol.2015142986
  9. Vernuccio F, Gagliano DS, Cannella R, et al. Spectrum of liver lesions hyperintense on hepatobiliary phase: an approach by clinical setting. Insights Imaging. 2021;12(1):8. doi: https://doi.org/10.1186/s13244-020-00928-w
  10. Kudo M, Matsui O, Izumi N, et al. Liver Cancer Study Group of Japan. JSH consensus-based clinical practice guidelines for the management of hepatocellular carcinoma: 2014 update by the Liver Cancer Study Group of Japan. Liver Cancer. 2014;3(3–4):458–468. doi: https://doi.org/10.1159/000343875
  11. Kitao A, Matsui O, Yoneda N, et al. The uptake transporter OATP8 expression decreases during multistep hepatocarcinogenesis: correlation with gadoxetic acid enhanced MR imaging. Eur Radiol. 2011;21(10):2056–2066. doi: https://doi.org/10.1007/s00330-011-2165-8
  12. Ba-Ssalamah A, Antunes C, Feier D, et al. Morphologic and molecular features of hepatocellular adenoma with gadoxetic acid-enhanced MR imaging. Radiology. 2015;277(1):104–113. doi: https://doi.org/10.1148/radiol.2015142366
  13. Holzapfel K, Bruegel M, Eiber M, et al. Characterization of small (≤10 mm) focal liver lesions: value of respiratory-triggered echo-planar diffusion-weighted MR imaging. Eur J Radiol. 2010;76(1):89–95. doi: https://doi.org/10.1016/j.ejrad.2009.05.014
  14. Вдовенко В.С., Карельская Н.А., Кондратьев Е.В., и др. Криодеструкция злокачественных образований печени: предварительные результаты МРТ-мониторинга на этапах лечения // Медицинская визуализация. — 2019. — № 1. — С. 8–18. [Vdovenko VS, Кагеlsкауа NA, Kondratyev EV, et al. Сryodestruсtion of m8lign8nt liver lesions: MRI monitoring of trestment, preliminsry results. Medical Visualization. 2019;1:8–18. (In Russ.)] doi: https://doi.org/10.24835/1607-0763-2019-1-8-18
  15. Ломовцева К.Х. Дифференциальная диагностика образований печени солидной структуры: роль диффузионно-взвешенных изображений и гепатоспецифичных контрастных средств: автореф. дис. ... канд. мед. наук. — М., 2019. — 24 с. [Lomovceva KH. Differencial’naya diagnostika obrazovanij pecheni solidnoj struktury: rol’ diffuzionno-vzveshennyh izobrazhenij i gepatospecifichnyh kontrastnyh sredstv: avtoref. dis. ... kand. med. nauk. Moscow; 2019. 24 s. (In Russ.)] Available from: https://www.sechenov.ru/upload/medialibrary/49e/AVTOREFERAT-v-pechat.pdf
  16. Jeong WK, Jamshidi N, Felker ER, et al. Radiomics and radiogenomics of primary liver cancers. Clin Mol Hepatol. 2019;25(1):21–29. doi: https://doi.org/10.3350/cmh.2018.100
  17. Raman SP, Schroeder JL, Huang P, et al. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements — a work in progress. J Comput Assist Tomogr. 2015;39(3):383–395. doi: https://doi.org/10.1097/RCT.0000000000000217
  18. Stocker D, Marquez HP, Wagner MW, et al. MRI texture analysis for differentiation of malignant and benign hepatocellular tumors in the non-cirrhotic liver. Heliyon. 2018;4(11):e00987. doi: https://doi.org/10.1016/j.heliyon.2018.e00987
  19. Martins-Filho SN, Paiva C, Azevedo RS, et al. Histological Grading of Hepatocellular Carcinoma-A Systematic Review of Literature. Front Med (Lausanne). 2017;4:193. doi: https://doi.org/10.3389/fmed.2017.00193
  20. Chen W, Zhang T, Xu L, et al. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol. 2021;11:660509. doi: https://doi.org/10.3389/fonc.2021.660509
  21. Chang N, Cui L, Luo Y, et al. Development and multicenter validation of a CT-based radiomics signature for discriminating histological grades of pancreatic ductal adenocarcinoma. Quant Imaging Med Surg. 2020;10(3):692–702. doi: https://doi.org/10.21037/qims.2020.02.21
  22. Qiu W, Duan N, Chen X, et al. Pancreatic Ductal Adenocarcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis For Prediction Of Histopathological Grade. Cancer Manag Res. 2019;11:9253–9264. doi: https://doi.org/10.2147/CMAR.S218414
  23. Yamashita R, Perrin T, Chakraborty J, et al. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol. 2020;30(1):195–205. doi: https://doi.org/10.1007/s00330-019-06381-8
  24. Тихонова В.С., Кармазановский Г.Г., Кондратьев Е.В., и др. Влияние параметров низкодозового протокола сканирования на результаты текстурного анализа протоковой аденокарциномы поджелудочной железы // Анналы хирургической гепатологии. — 2021. — Т. 26. — № 1. — С. 25–33. [Tikhonova VS, Karmazanovsky GG, Kondratyev EV, et al. Influence of the low-dose CE-MDCT scanning protocol parameters on the results of pancreatic ductal adenocarcinoma radiomic analysis. Annaly khirurgicheskoy gepatologii = Annals of HPB Surgery. 2021;26(1):25–33. (In Russ.)] doi: https://doi.org/10.16931/1995-5464.2021125-33
  25. Eilaghi A, Baig S, Zhang Y, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma — a quantitative analysis. BMC Med Imaging. 2017;17(1):38. doi: https://doi.org/10.1186/s12880-017-0209-5
  26. Sandrasegaran K, Lin Y, Asare-Sawiri M, et al. CT texture analysis of pancreatic cancer. Eur Radiol. 2019;29(3):1067–1073. doi: https://doi.org/10.1007/s00330-018-5662-1
  27. Kulkarni A, Carrion-Martinez I, Jiang NN, et al. Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features. Eur Radiol. 2020;30(5):2853–2860. doi: https://doi.org/10.1007/s00330-019-06583-0
  28. Yun G, Kim YH, Lee YJ, et al. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep. 2018;8(1):7226. doi: https://doi.org/10.1038/s41598-018-25627-x
  29. Fang WH, Li XD, Zhu H, et al. Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement. Cancer Imaging. 2020;20(1):17. doi: https://doi.org/10.1186/s40644-020-0296-3
  30. Park S, Chu LC, Hruban RH, et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging. 2020;101(9):555–564. doi: https://doi.org/10.1016/j.diii.2020.03.002
  31. Zaheer A, Singh VK, Akshintala VS, et al. Differentiating autoimmune pancreatitis from pancreatic adenocarcinoma using dual-phase computed tomography. J Comput Assist Tomogr. 2014;38(1):146–152. doi: https://doi.org/10.1097/RCT.0b013e3182a9a431
  32. Zhang JJ, Li QZ, Wang JH, et al. Contrast-enhanced CT and texture analysis of mass-forming pancreatitis and cancer in the pancreatic head. Zhonghua Yi Xue Za Zhi. 2019;99(33):2575–2580. Chinese. doi: https://doi.org/10.3760/cma.j.issn.0376-2491.2019.33.004
  33. Ren S, Zhao R, Zhang J, et al. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol (NY). 2020;45(5):1524–1533. doi: https://doi.org/10.1007/s00261-020-02506-6
  34. Wolske KM, Ponnatapura J, Kolokythas O, et al. Chronic Pancreatitis or Pancreatic Tumor? A Problem-solving Approach. Radiographics. 2019;39(7):1965–1982. doi: https://doi.org/10.1148/rg.2019190011
  35. Ren S, Zhao R, Zhang J, et al. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol (NY). 2020;45(5):1524–1533. doi: https://doi.org/10.1007/s00261-020-02506-6
  36. Ciaravino V, Cardobi N, De Robertis R, et al. CT Texture Analysis of Ductal Adenocarcinoma Downstaged After Chemotherapy. Anticancer Res. 2018;38(8):4889–4895. doi: https://doi.org/10.21873/anticanres.12803
  37. Gruzdev IS, Zamyatina KA, Tikhonova VS, et al. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol. 2020;133:109371. doi: https://doi.org/10.1016/j.ejrad.2020.109371
  38. Zhao B, Tan Y, Tsai WY, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428. doi: https://doi.org/10.1038/srep23428
  39. Pavic M, Bogowicz M, Würms X, et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol. 2018;57(8):1070–1074. doi: https://doi.org/10.1080/0284186X.2018.1445283
  40. Белоусова Е.Л., Кармазановский Г.Г., Кубышкин В.А., и др. КТ-признаки, позволяющие определить оптимальную тактику лечения при нейроэндокринных опухолях поджелудочной железы // Медицинская визуализация. — 2015. — № 5. — С. 73–82. [Belousova EL, Karmazanovsky GG, Kubyshkin VA, et al. CT Features Predict the Optimal Therapeutic Approach for Pancreatic Neuroendocrine Neoplasms. Medical Visualization. 2015;5:73–82. (In Russ.)] Available from: https://medvis.vidar.ru/jour/article/view/231
  41. Belousova E, Karmazanovsky G, Kriger A, et al. Contrast-enhanced MDCT in patients with pancreatic neuroendocrine tumours: correlation with histological findings and diagnostic performance in differentiation between tumour grades. Clin Radiol. 2017;72(2):150–158. doi: https://doi.org/10.1016/j.crad.2016.10.021
  42. Груздев И.С., Тихонова В.С., Замятина К.А., и др. Компьютерная томография в прогнозировании степени дифференцировки гиперваскулярных нейроэндокринных опухолей поджелудочной железы: текстурный анализ и характеристики контрастирования // REJR. — 2021. — Т. 11. — № 4. — С. 105–114. [Gruzdev IS, Tikhonova VS, Zamyatina KA, et al. Computed tomography in prediction of hypervascular pancreatic neuroendocrine tumors grade: texture analysis and contrast enhancement features. REJR. 2021;11(4):105–114. (In Russ.)] doi: https://doi.org/10.21569/2222-7415-2021-11-4-105-114
  43. Lin X, Xu L, Wu A, et al. Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol. 2019;60(5):553–560. doi: https://doi.org/10.1177/0284185118788895
  44. Van der Pol CB, Lee S, Tsai S, et al. Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdom Radiol (NY). 2019;44(3):992–999. doi: https://doi.org/10.1007/s00261-018-01889-x
  45. Liu KL, Wu T, Chen PT, et al. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020;2(6):e303–e313. doi: https://doi.org/10.1016/S2589-7500(20)30078-9
  46. Chan HP, Samala RK, Hadjiiski LM, et al. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020;1213:3–21. doi: https://doi.org/10.1007/978-3-030-33128-3_1
  47. Yasaka K, Abe O. Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS Med. 2018;15(11):e1002707. doi: https://doi.org/10.1371/journal.pmed.1002707
  48. Yasaka K, Akai H, Abe O, et al. Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. Radiology. 2018;286(3):887–896. doi: https://doi.org/10.1148/radiol.2017170706
  49. Kim K, Kim S, Han K, et al. Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer. Korean J Radiol. 2021;22(6):912–921. doi: https://doi.org/10.3348/kjr.2020.0447

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