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Radiomic applications in upper gastrointestinal cancer surgery

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Langenbeck's Archives of Surgery Aims and scope Submit manuscript

Abstract

Introduction

Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care.

Objective

Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy.

Conclusion

Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.

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Abbreviations

AI:

Artificial intelligence

ALBI:

Albumin-bilirubin score

AUC:

Area under the receiver operating characteristic curve

CT:

Computed tomography

FDG:

Fludeoxyglucose

FLRV:

Future liver remnant volume

Gd-EOB-DTPA:

Gadolinium-ethoxybenzyl-diethylenetriamine

GLCM:

Grey-level co-occurrence matrix

GLDM:

Grey-level dependence matrix

GLDZM:

Grey-level distance zone matrix

GLRLM:

Grey-level run-length matrix

GLSZM:

Grey-level size zone matrix

GOJ:

Gastro-oesophageal junction

HCC:

Hepatocellular carcinoma

IBSI:

Imaging Biomarker Standardisation Initiative

ICC:

Intra-class correlation coefficient

ICG:

Indocyanine green

LASSO:

Least absolute shrinkage and selection operator

MELD:

Model for end-stage liver disease

MRI:

Magnetic resonance imaging

mRMR:

Minimum redundancy maximum relevance

nCRT:

Neoadjuvant chemoradiotherapy

NGLDM:

Neighbourhood grey-level dependence matrix

NGTDM:

Neighbourhood grey-tone difference matrix

pCR:

Complete pathological response

PDAC:

Pancreatic ductal adenocarcinoma

PET:

Positron emission tomography

PHLF:

Post-operative liver failure

PHM:

Pancreatic head malignancy

ROI:

Region of interest

SCC:

Squamous cell carcinoma

SMA:

Superior mesenteric artery

SMV:

Superior mesenteric vein

TRG:

Tumour regression grade

UGI:

Upper gastrointestinal

References

  1. Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S (2021) Radiomics and machine learning applications in rectal cancer: current update and future perspectives. WJG 27(32):5306–5321

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446

    Article  PubMed  PubMed Central  Google Scholar 

  3. Alderson PO, Summers RM (2020) The evolving status of radiomics. JNCI: J Natl Cancer Inst 112(9):869–70

    Article  PubMed  PubMed Central  Google Scholar 

  4. Yip SSF, Aerts HJWL (2016) Applications and limitations of radiomics. Phys Med Biol 61(13):R150–R166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wang Y, Jin ZY (2019) Radiomics approaches in gastric cancer: a frontier in clinical decision making. Chin Med J 132(16):1983–1989

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shur JD, Doran SJ, Kumar S, apDafydd D, Downey K, O’Connor JPB et al (2021) Radiomics in oncology: a practical guide. RadioGraphics 41(6):1717–32

    Article  PubMed  Google Scholar 

  7. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P et al (2020) Introduction to radiomics. J Nucl Med 61(4):488–495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG et al (2018) Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2(1):36

    Article  PubMed  PubMed Central  Google Scholar 

  9. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging 11(1):91

    Article  PubMed  PubMed Central  Google Scholar 

  10. Shaikh FA, Kolowitz BJ, Awan O, Aerts HJ, von Reden A, Halabi S et al (2017) Technical challenges in the clinical application of radiomics. JCO Clin Cancer Inf 1:1–8

    Google Scholar 

  11. Mu W, Schabath MB, Gillies RJ (2022) Images are data: challenges and opportunities in the clinical translation of radiomics. Can Res 82(11):2066–2068

    Article  CAS  Google Scholar 

  12. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: the promise of radiomics. Physica Med 38:122–139

    Article  Google Scholar 

  13. Alderson PO (2020) The quest for generalizability in radiomics. Radiology: Artif Intell 2(3):e200068

    Google Scholar 

  14. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V et al (2017) Computational radiomics system to decode the radiographic phenotype. Can Res 77(21):e104–e107

    Article  Google Scholar 

  15. Bibault JE, Xing L, Giraud P, El Ayachy R, Giraud N, Decazes P et al (2020) Radiomics: a primer for the radiation oncologist. Cancer/Radiothérapie 24(5):403–410

    Article  PubMed  Google Scholar 

  16. Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R (2019) Radiogenomics: bridging imaging and genomics. Abdom Radiol 44(6):1960–1984

    Article  Google Scholar 

  17. Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26(6):565–574

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wu L, Wang C, Tan X, Cheng Z, Zhao K, Yan L et al (2018) Radiomics approach for preoperative identification of stages I−II and III−IV of esophageal cancer. Chin J Cancer Res 30(4):396–405

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kawahara D, Murakami Y, Tani S, Nagata Y (2021) A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning. BJR 94(1124):20210525

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gao X, Ma T, Cui J, Zhang Y, Wang L, Li H et al (2021) A CT-based radiomics model for prediction of lymph node metastasis in early stage gastric cancer. Acad Radiol 28(6):e155–e164

    Article  PubMed  Google Scholar 

  21. Wang Y, Liu W, Yu Y, Liu JJ, Xue HD, Qi YF et al (2020) CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Eur Radiol. 30(2):976–86

    Article  PubMed  Google Scholar 

  22. Sun Z, Jiang Y, Chen C, Zheng H, Huang W, Xu B et al (2021) Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: a multicenter study. Radiother Oncol 165:179–190

    Article  PubMed  Google Scholar 

  23. Dong D, Fang MJ, Tang L, Shan XH, Gao JB, Giganti F et al (2020) Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol 31(7):912–920

    Article  CAS  PubMed  Google Scholar 

  24. Zhao B, Zhu HT, Li XT, Shi YJ, Cao K, Sun YS (2021) Predicting lymph node metastasis using computed tomography radiomics analysis in patients with resectable esophageal squamous cell carcinoma. J Comput Assist Tomogr 45(2):323–329

    Article  PubMed  Google Scholar 

  25. Shen C, Liu Z, Wang Z, Guo J, Zhang H, Wang Y et al (2018) Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction. Translational Oncology 11(3):815–824

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chen Y, Xi W, Yao W, Wang L, Xu Z, Wels M et al (2021) Dual-energy computed tomography-based radiomics to predict peritoneal metastasis in gastric cancer. Front Oncol 14(11):659981

    Article  Google Scholar 

  27. Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X et al (2021) Development and validation of a radiomics model based on 18F-FDG PET of primary gastric cancer for predicting peritoneal metastasis. Front Oncol 26(11):740111

    Article  Google Scholar 

  28. Peng H, Xue T, Chen Q, Li M, Ge Y, Feng F (2022) Computed tomography-based radiomics nomogram for predicting the postoperative prognosis of esophageal squamous cell carcinoma: a multicenter study. Acad Radiol S1076-6332(22)00070-8

  29. Tang S, Ou J, Wu YP, Li R, Chen TW, Zhang XM (2021) Contrast-enhanced CT radiomics features to predict recurrence of locally advanced oesophageal squamous cell cancer within 2 years after trimodal therapy: a case-control study. Medicine 100(27):e26557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tang S, Ou J, Liu J, Wu YP, Wu CQ, Chen TW et al (2021) Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy. Cancer Imaging 21(1):38

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kong J, Zhu S, Shi G, Liu Z, Zhang J, Ren J (2021) Prediction of locoregional recurrence-free survival of oesophageal squamous cell carcinoma after chemoradiotherapy based on an enhanced CT-based radiomics model. Front Oncol 24(11):739933

    Article  Google Scholar 

  32. Luo HS, Chen YY, Huang WZ, Wu SX, Huang SF, Xu HY et al (2021) Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer. Radiat Oncol 16(1):201

    Article  PubMed  PubMed Central  Google Scholar 

  33. Deantonio L, Garo ML, Paone G, Valli MC, Cappio S, La Regina D et al (2022) 18F-FDG PET radiomics as predictor of treatment response in oesophageal cancer: a systematic review and meta-analysis. Front Oncol 15(12):861638

    Article  Google Scholar 

  34. van Rossum PSN, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ et al (2016) The incremental value of subjective and quantitative assessment of 18 F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med 57(5):691–700

    Article  PubMed  Google Scholar 

  35. Yip SSF, Coroller TP, Sanford NN, Mamon H, Aerts HJWL, Berbeco RI (2016) Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients. Front Oncol 6:72

  36. Eyck BM, van der Wilk BJ, Lagarde SM, Wijnhoven BPL, Valkema R, Spaander MCW, Nuyttens JJME, van der Gaast A, van Lanschot JJB (2018) Neoadjuvant chemoradiotherapy for resectable oesophageal cancer. Best Pract Res Clin Gastroenterol 36–37:37–44

    Article  PubMed  Google Scholar 

  37. Beukinga RJ, Hulshoff JB, van Dijk LV, Muijs CT, Burgerhof JGM, Kats-Ugurlu G et al (2017) Predicting response to neoadjuvant chemoradiotherapy in esophageal cancer with textural features derived from pretreatment 18 F-FDG PET/CT imaging. J Nucl Med 58(5):723–729

    Article  CAS  PubMed  Google Scholar 

  38. Hirata A, Hayano K, Ohira G, Imanishi S, Hanaoka T, Murakami K et al (2020) Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy. Am J Surg 219(6):1024–1029

    Article  PubMed  Google Scholar 

  39. Yang Z, He B, Zhuang X, Gao X, Wang D, Li M et al (2019) CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy. J Radiat Res 60(4):538–545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rishi A, Zhang GG, Yuan Z, Sim AJ, Song EY, Moros EG et al (2021) Pretreatment CT and 18 F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer. J Med Imag Rad Onc 65(1):102–111

    Article  Google Scholar 

  41. Hu Y, Xie C, Yang H, Ho JWK, Wen J, Han L et al (2020) Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. JAMA Netw Open 3(9):e2015927

    Article  PubMed  PubMed Central  Google Scholar 

  42. Murakami Y, Kawahara D, Tani S, Kubo K, Katsuta T, Imano N et al (2021) Predicting the local response of esophageal squamous cell carcinoma to neoadjuvant chemoradiotherapy by radiomics with a machine learning method using 18F-FDG PET images. Diagnostics 11(6):1049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhu WS, Shi SY, Yang ZH, Song C, Shen J (2020) Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure. WJG 26(11):1208–1220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen Y, Liu Z, Mo Y, Li B, Zhou Q, Peng S et al (2021) Prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma based on radiomics using Gd-EOB-DTPA-enhanced MRI: the liver failure model. Front Oncol 10(11):605296

    Article  Google Scholar 

  45. Søreide JA, Deshpande R (2021) Post hepatectomy liver failure (PHLF) – recent advances in prevention and clinical management. Eur J Surg Oncol 47(2):216–224

    Article  PubMed  Google Scholar 

  46. Cai W, He B, Hu M, Zhang W, Xiao D, Yu H et al (2019) A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma. Surg Oncol 28:78–85

    Article  PubMed  Google Scholar 

  47. Xiang F, Liang X, Yang L, Liu X, Yan S (2021) CT radiomics nomogram for the preoperative prediction of severe post-hepatectomy liver failure in patients with huge (≥ 10 cm) hepatocellular carcinoma. World J Surg Onc 19(1):344

    Article  Google Scholar 

  48. Hanafy AS (2021) Prediction and prevention of post-hepatectomy liver failure: where do we stand? J Clin Transl Hepatol 000(000):000–000

    Article  Google Scholar 

  49. Versteijne E, Vogel JA, Besselink MG, Busch ORC, Wilmink JW, Daams JG et al (2018) Meta-analysis comparing upfront surgery with neoadjuvant treatment in patients with resectable or borderline resectable pancreatic cancer. Br J Surg 105(8):946–958

    Article  CAS  PubMed  Google Scholar 

  50. Maeda S, Moore AM, Yohanathan L, Hata T, Truty MJ, Smoot RL et al (2020) Impact of resection margin status on survival in pancreatic cancer patients after neoadjuvant treatment and pancreatoduodenectomy. Surgery 167(5):803–811

    Article  PubMed  Google Scholar 

  51. Fukuda Y, Yamada D, Eguchi H, Hata T, Iwagami Y, Noda T et al (2017) CT density in the pancreas is a promising imaging predictor for pancreatic ductal adenocarcinoma. Ann Surg Oncol 24(9):2762–2769

    Article  PubMed  Google Scholar 

  52. Weyhe D, Obonyo D, Uslar VN, Stricker I, Tannapfel A (2021) Predictive factors for long-term survival after surgery for pancreatic ductal adenocarcinoma: making a case for standardized reporting of the resection margin using certified cancer center data. Wellner U, editor. PLoS ONE 16(3):e0248633

  53. Ocaña J, Sanjuanbenito A, García A, Molina JM, Lisa E, Mendía E et al (2020) Relevance of positive resection margins in ductal pancreatic adenocarcinoma and prognostic factors. Cirugía Española (English Edition) 98(2):85–91

    Article  Google Scholar 

  54. Menon KV, Gomez D, Smith AM, Anthoney A, Verbeke CS (2009) Impact of margin status on survival following pancreatoduodenectomy for cancer: the Leeds Pathology Protocol (LEEPP). HPB 11(1):18–24

    Article  PubMed  PubMed Central  Google Scholar 

  55. Liu KL, Wu T, Chen PT, Tsai YM, Roth H, Wu MS et al (2020) Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digital Health 2(6):e303–e313

    Article  PubMed  Google Scholar 

  56. Cassinotto C, Dohan A, Zogopoulos G, Chiche L, Laurent C, Sa-Cunha A et al (2017) Pancreatic adenocarcinoma: a simple CT score for predicting margin-positive resection in patients with resectable disease. Eur J Radiol 95:33–38

    Article  PubMed  Google Scholar 

  57. Isaji S, Mizuno S, Windsor JA, Bassi C, Fernández-del Castillo C, Hackert T et al (2018) International consensus on definition and criteria of borderline resectable pancreatic ductal adenocarcinoma 2017. Pancreatology 18(1):2–11

    Article  PubMed  Google Scholar 

  58. Kobi M, Veillette G, Narurkar R, Sadowsky D, Paroder V, Shilagani C et al (2020) Imaging and management of pancreatic cancer. Sem Ultrasound CT MRI 41(2):139–151

    Article  Google Scholar 

  59. Lopez NE (2014) Borderline resectable pancreatic cancer: definitions and management. WJG 20(31):10740

    Article  PubMed  PubMed Central  Google Scholar 

  60. Bian Y, Jiang H, Ma C, Cao K, Fang X, Li J et al (2020) Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer. Abdom Radiol 45(3):759–773

    Article  Google Scholar 

  61. Rigiroli F, Hoye J, Lerebours R, Lafata KJ, Li C, Meyer M et al (2021) CT radiomic features of superior mesenteric artery involvement in pancreatic ductal adenocarcinoma: a pilot study. Radiology 7:210699

    Google Scholar 

  62. Lin Z, Tang B, Cai J, Wang X, Li C, Tian X, Yang Y, Wang X (2021) Preoperative prediction of clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy. Eur J Radiol 139:109693

    Article  PubMed  Google Scholar 

  63. Zhang W, Cai W, He B, Xiang N, Fang C, Jia F (2018) A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy. Cancer Manag Res 28(10):6469–6478

    Article  Google Scholar 

  64. Skawran SM, Kambakamba P, Baessler B, von Spiczak J, Kupka M, Müller PC, Moeckli B, Linecker M, Petrowsky H, Reiner CS (2021) Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula? Eur J Radiol 140:109733

    Article  PubMed  Google Scholar 

  65. Lee CH, Yoon HJ (2017) Medical big data: promise and challenges. Kidney Res Clin Pract 36(1):3–11

    Article  PubMed  PubMed Central  Google Scholar 

  66. de la Pinta C (2021) Radiomics in pancreatic cancer for oncologist: present and future. Hepatobiliary Pancreat Dis Int 21(4):356–361

  67. Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31(2):1049–1058

    Article  PubMed  Google Scholar 

  68. Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, Shin JH, Kim JH (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30(1):523–536

    Article  PubMed  Google Scholar 

  69. Lambin P. Radiomics quality score - RQS. Available from: https://www.radiomics.world (Accessed: May 2023)

  70. Fanciullo C, Gitto S, Carlicchi E, Albano D, Messina C, Sconfienza LM (2022) Radiomics of musculoskeletal sarcomas: a narrative review. J Imaging 8(2):45

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chen B, Yang L, Zhang R, Luo W, Li W (2020) Radiomics: an overview in lung cancer management—a narrative review. Ann Transl Med 8(18):1191–1191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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    Authors and Affiliations

    1. Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK

      Joseph P. Doyle, Pranav H. Patel, Nikoletta Petrou, Sacheen Kumar & Ricky H. Bhogal

    2. Department of Radiology, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK

      Joshua Shur & Matthew Orton

    3. Upper GI Surgical Oncology Research Group, The Institute for Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK

      Sacheen Kumar & Ricky H. Bhogal

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    SK and RB provided the concept for the paper. JD wrote the main manuscript, prepared tables and figures. All authors reviewed and critically revised the manuscript before final submission.

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    Correspondence to Ricky H. Bhogal.

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    Key points

    • Radiomic studies to date have shown promising utility in upper gastrointestinal oncology.

    • The powerful predictive capacity of radiomics has potential to guide surgical decision-making.

    • Further prospective research is required to integrate radiomic modelling into clinical pathways in order to impact patient outcomes.

    Sacheen Kumar and Ricky H. Bhogal are joint senior authors.

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    Doyle, J.P., Patel, P.H., Petrou, N. et al. Radiomic applications in upper gastrointestinal cancer surgery. Langenbecks Arch Surg 408, 226 (2023). https://doi.org/10.1007/s00423-023-02951-z

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