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PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival

European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

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Abstract

Purpose

Intrahepatic cholangiocarcinoma (IHC) is an aggressive disease with few reliable preoperative biomarkers. This study aims to elucidate if radiomics extracted from preoperative [18F]FDG PET/CT may grant a non-invasive biological characterization of IHC and predict outcome after complete resection of the tumor.

Methods

All patients preoperatively imaged by [18F]FDG PET/CT who underwent hepatectomy for mass-forming IHC in the period 2010–2019 were retrospectively evaluated. On PET images, manual slice-by-slice segmentation of IHC was performed (Tumor-VOI). A 5-mm margin region was semi-automatically generated around the tumor (Margin-VOI). Textural analysis was performed using the LifeX software. Analyzed outcomes included tumor grading (G3 vs. G1-2), microvascular invasion (MVI), overall survival (OS), and progression-free survival (PFS). The performances of the combined clinical-radiomic models were compared with those of standard clinical models.

Results

Overall, 74 patients (40 females, median age 68 years) were included. Considering tumor grading and MVI, the models combining the clinical data and radiomics of the Tumor-VOI had better performances than the clinical ones (AUC = 0.78 vs. 0.72 for grading; 0.87 vs. 0.78 for MVI). The inclusion into the models of radiomics of the Margin-VOI further improved the prediction of grading (AUC = 0.83), but not of MVI. Considering OS and PFS, the models including the preoperative clinical data and radiomics of the Tumor-VOI and Margin-VOI had better performances than the pure clinical ones (C-index = 0.81 vs. 0.76 for OS; 0.81 vs. 0.72 for PFS) and similar to the models including the pathology and postoperative data (C-index = 0.81 for OS; 0.79 for PFS). No model retained the standard SUV measures.

Conclusion

The PET-based radiomics of IHC can predict pathology data and allow a reliable preoperative evaluation of prognosis. The radiomics of both the tumoral and peritumoral areas had clinical relevance. The combined clinical-radiomic models outperformed the pure preoperative clinical ones and achieved performances non-inferior to the postoperative models.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Rizvi S, Khan SA, Hallemeier CL, Kelley RK, Gores GJ. Cholangiocarcinoma - evolving concepts and therapeutic strategies. Nat Rev Clin Oncol. 2018;15:95–111.

    Article  CAS  Google Scholar 

  2. Saha SK, Zhu AX, Fuchs CS, Brooks GA. Forty-year trends in cholangiocarcinoma incidence in the U.S.: intrahepatic disease on the rise. Oncologist. 2016;21:594–9.

    Article  Google Scholar 

  3. Banales JM, Cardinale V, Carpino G, Marzioni M, Andersen JB, Invernizzi P, Lind GE, Folseraas T, Forbes SJ, Fouassier L, Geier A, Calvisi DF, Mertens JC, Trauner M, Benedetti A, Maroni L, Vaquero J, Macias RI, Raggi C, Perugorria MJ, Gaudio E, Boberg KM, Marin JJ, Alvaro D. Expert consensus document: cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA). Nat Rev Gastroenterol Hepatol. 2016;13:261–80.

    Article  Google Scholar 

  4. Mazzaferro V, Gorgen A, Roayaie S, Droz Dit Busset M, Sapisochin G. Liver resection and transplantation for intrahepatic cholangiocarcinoma. J Hepatol. 2020;72:364–77.

  5. Torzilli G, Viganò L, Fontana A, Procopio F, Terrone A, Cimino MM, Donadon M, Del Fabbro D. Oncological outcome of R1 vascular margin for mass-forming cholangiocarcinoma. A single center observational cohort analysis. HPB. 2020;22:570–7.

    Article  Google Scholar 

  6. Conci S, Viganò L, Ercolani G, Gonzalez E, Ruzzenente A, Isa G, Salaris C, Fontana A, Bagante F, Pedrazzani C, Campagnaro T, Iacono C, De Santibanes E, Pinna DA, Torzilli G, Guglielmi A. Outcomes of vascular resection associated with curative intent hepatectomy for intrahepatic cholangiocarcinoma. Eur J Surg Oncol. 2020;46:1727–33.

    Article  Google Scholar 

  7. Hue JJ, Rocha FG, Ammori JB, Hardacre JM, Rothermel LD, Chavin KD, Winter JM, Ocuin LM. A comparison of surgical resection and liver transplantation in the treatment of intrahepatic cholangiocarcinoma in the era of modern chemotherapy: an analysis of the National Cancer Database. J Surg Oncol. 2021;123:949–56.

    Article  Google Scholar 

  8. Bagante F, Spolverato G, Weiss M, Alexandrescu S, Marques HP, Aldrighetti L, Maithel SK, Pulitano C, Bauer TW, Shen F, Poultsides GA, Soubrane O, Martel G, Groot Koerkamp B, Guglielmi A, Itaru E, Pawlik TM. Defining long-term survivors following resection of intrahepatic cholangiocarcinoma. J Gastrointest Surg. 2017;21:1888–97.

    Article  Google Scholar 

  9. Doussot A, Gonen M, Wiggers JK, Groot-Koerkamp B, DeMatteo RP, Fuks D, Allen PJ, Farges O, Kingham TP, Regimbeau JM, D’Angelica MI, Azoulay D, Jarnagin WR. Recurrence patterns and disease-free survival after resection of intrahepatic cholangiocarcinoma: preoperative and postoperative prognostic models. J Am Coll Surg. 2016;223:493–505.

    Article  Google Scholar 

  10. Mavros MN, Economopoulos KP, Alexiou VG, Pawlik TM. Treatment and prognosis for patients with intrahepatic cholangiocarcinoma: systematic review and meta-analysis. JAMA Surg. 2014;149:565–74.

    Article  Google Scholar 

  11. Tsukamoto M, Yamashita YI, Imai K, Umezaki N, Yamao T, Okabe H, Nakagawa S, Hashimoto D, Chikamoto A, Ishiko T, Yoshizumi T, Maehara Y, Baba H. Predictors of cure of intrahepatic cholangiocarcinoma after hepatic resection. Anticancer Res. 2017;37:6971–5.

    PubMed  Google Scholar 

  12. Fabris L, Sato K, Alpini G, Strazzabosco M. The tumor microenvironment in cholangiocarcinoma progression. Hepatology. 2021;73:75–85.

    Article  Google Scholar 

  13. Job S, Rapoud D, Dos Santos A, Gonzalez P, Desterke C, Pascal G, Elarouci N, Ayadi M, Adam R, Azoulay D, Castaing D, Vibert E, Cherqui D, Samuel D, Sa Cuhna A, Marchio A, Pineau P, Guettier C, de Reyniès A, Faivre J. Identification of four immune subtypes characterized by distinct composition and functions of tumor microenvironment in intrahepatic cholangiocarcinoma. Hepatology. 2020;72:965–81.

    Article  CAS  Google Scholar 

  14. Vigano L, Soldani C, Franceschini B, Cimino M, Lleo A, Donadon M, Roncalli M, Aghemo A, Di Tommaso L, Torzilli G. Tumor-infiltrating lymphocytes and macrophages in intrahepatic cholangiocellular carcinoma. Impact on prognosis after complete surgery. J Gastrointest Surg. 2019;23:2216–24.

    Article  Google Scholar 

  15. 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. Nat Rev Clin Oncol. 2017;14:749–62.

    Article  Google Scholar 

  16. Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles published in the emerging field of radiomics. Eur J Radiol. 2020;127:108991.

    Article  Google Scholar 

  17. Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging. 2019;46:2656–72.

    Article  Google Scholar 

  18. Fiz F, Viganò L, Gennaro N, Costa G, La Bella L, Boichuk A, Cavinato L, Sollini M, Politi LS, Chiti A, Torzilli G. Radiomics of liver metastases: a systematic review. Cancers. 2020;12:2881.

    Article  CAS  Google Scholar 

  19. Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C, Felli E, Saviano A, Agnus V, Savadjiev P, Baumert TF, Pessaux P, Marescaux J, Gallix B. Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int. 2019;13:546–59.

    Article  Google Scholar 

  20. Ji GW, Zhu FP, Zhang YD, Liu XS, Wu FY, Wang K, Xia YX, Zhang YD, Jiang WJ, Li XC, Wang XH. A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma. Eur Radiol. 2019;29:3725–35.

    Article  Google Scholar 

  21. Park HJ, Park B, Park SY, Choi SH, Rhee H, Park JH, Cho ES, Yeom SK, Park S, Park MS, Lee SS. Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features. Eur Radiol. 2021;31:8638–48.

    Article  Google Scholar 

  22. Zhang J, Wang X, Zhang L, Yao L, Xue X, Zhang S, Li X, Chen Y, Pang P, Sun D, Xu J, Shi Y, Chen F. Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types. Ann Transl Med. 2020;8:820.

    Article  CAS  Google Scholar 

  23. Yoh T, Seo S, Morino K, Fuji H, Ikeno Y, Ishii T, Taura K, Nakamoto Y, Higashi T, Kaido T, Uemoto S. Reappraisal of prognostic impact of tumor SUVmax by 18F-FDG-PET/CT in intrahepatic cholangiocarcinoma. World J Surg. 2019;43:1323–31.

    Article  Google Scholar 

  24. Ikeno Y, Seo S, Iwaisako K, Yoh T, Nakamoto Y, Fuji H, Taura K, Okajima H, Kaido T, Sakaguchi S, Uemoto S. Preoperative metabolic tumor volume of intrahepatic cholangiocarcinoma measured by 18F-FDG-PET is associated with the KRAS mutation status and prognosis. J Transl Med. 2018;16:95.

    Article  CAS  Google Scholar 

  25. Harimoto N, Hoshino K, Muranushi R, Hagiwara K, Yamanaka T, Ishii N, Tsukagoshi M, Igarashi T, Tanaka H, Watanabe A, Kubo N, Araki K, Tomonaga H, Higuchi T, Tsushima Y, Shirabe K. Impact of metabolic parameters of 18F-fluorodeoxyglucose positron-emission tomography after hepatic resection in patients with intrahepatic cholangiocarcinoma. Anticancer Res. 2019;39:971–7.

    Article  CAS  Google Scholar 

  26. Boellaard R, O'Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, Oyen WJ, Kotzerke J, Hoekstra OS, Pruim J, Marsden PK, Tatsch K, Hoekstra CJ, Visser EP, Arends B, Verzijlbergen FJ, Zijlstra JM, Comans EF, Lammertsma AA, Paans AM, Willemsen AT, Beyer T, Bockisch A, Schaefer-Prokop C, Delbeke D, Baum RP, Chiti A, Krause BJ. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging. 2010;37:181–200.

  27. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, Verzijlbergen FJ, Barrington SF, Pike LC, Weber WA, Stroobants S, Delbeke D, Donohoe KJ, Holbrook S, Graham MM, Testanera G, Hoekstra OS, Zijlstra J, Visser E, Hoekstra CJ, Pruim J, Willemsen A, Arends B, Kotzerke J, Bockisch A, Beyer T, Chiti A, Krause BJ. European Association of Nuclear Medicine (EANM). FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.

    Article  CAS  Google Scholar 

  28. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786–9.

    Article  CAS  Google Scholar 

  29. Viganò L, Lleo A, Muglia R, Gennaro N, Samà L, Colapietro F, Roncalli M, Aghemo A, Chiti A, Di Tommaso L, Solbiati L, Colombo M, Torzilli G. Intrahepatic cholangiocellular carcinoma with radiological enhancement patterns mimicking hepatocellular carcinoma. Updates Surg. 2020;72:413–21.

    Article  Google Scholar 

  30. Torzilli G. Ultrasound-guided liver surgery. 1st ed. Springer; 2014.

  31. Baheti AD, Tirumani SH, Shinagare AB, Rosenthal MH, Hornick JL, Ramaiya NH, Wolpin BM. Correlation of CT patterns of primary intrahepatic cholangiocarcinoma at the time of presentation with the metastatic spread and clinical outcomes: retrospective study of 92 patients. Abdom Imaging. 2014;39:1193–201.

    Article  Google Scholar 

  32. Conci S, Ruzzenente A, Viganò L, Ercolani G, Fontana A, Bagante F, Bertuzzo F, Dore A, Pinna AD, Torzilli G, Iacono C, Guglielmi A. Patterns of distribution of hepatic nodules (single, satellites or multifocal) in intrahepatic cholangiocarcinoma: prognostic impact after surgery. Ann Surg Oncol. 2018;25:3719–27.

    Article  Google Scholar 

  33. Yang C, Huang M, Li S, Chen J, Yang Y, Qin N, Huang D, Shu J. Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma. Cancer Lett. 2020;470:1–7.

    Article  CAS  Google Scholar 

  34. Yao X, Huang X, Yang C, Hu A, Zhou G, Lei J, Shu J. A novel approach to assessing differentiation degree and lymph node metastasis of extrahepatic cholangiocarcinoma: prediction using a radiomics-based particle swarm optimization and support vector machine Model. JMIR Med Inform. 2020;8:e23578.

    Article  Google Scholar 

  35. Zhou Y, Zhou G, Zhang J, Xu C, Wang X, Xu P. Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Eur Radiol. 2021;31:6846–55.

    Article  Google Scholar 

  36. Xu L, Yang P, Liang W, Liu W, Wang W, Luo C, Wang J, Peng Z, Xing L, Huang M, Zheng S, Niu T. A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics. 2019;9:5374–85.

    Article  Google Scholar 

  37. Rahmim A, Bak-Fredslund KP, Ashrafinia S, Lu L, Schmidtlein CR, Subramaniam RM, Morsing A, Keiding S, Horsager J, Munk OL. Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features. Eur J Radiol. 2019;113:101–9.

    Article  Google Scholar 

  38. Fiz F, Costa G, Gennaro N, la Bella L, Boichuk A, Sollini M, Politi LS, Balzarini L, Torzilli G, Chiti A, Viganò L. Contrast administration impacts CT-based radiomics of colorectal liver metastases and non-tumoral liver parenchyma revealing the “radiological” tumour microenvironment. Diagnostics. 2021;11:1162.

    Article  Google Scholar 

  39. Yugawa K, Itoh S, Iseda N, Kurihara T, Kitamura Y, Toshima T, Harada N, Kohashi K, Baba S, Ishigami K, Oda Y, Yoshizumi T, Mori M. Obesity is a risk factor for intrahepatic cholangiocarcinoma progression associated with alterations of metabolic activity and immune status. Sci Rep. 2021;11:5845.

    Article  CAS  Google Scholar 

  40. Lamarca A, Barriuso J, Chander A, McNamara MG, Hubner RA, ÓReilly D, Manoharan P, Valle JW. 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET) for patients with biliary tract cancer: systematic review and meta-analysis. J Hepatol. 2019;71:115–29.

  41. Sollini M, Kirienko M, Gelardi F, Fiz F, Gozzi N, Chiti A. State-of-the-art of FAPI-PET imaging: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging. 2021;48:4396–414.

    Article  Google Scholar 

  42. Nakanishi R, Oki E, Hasuda H, Sano E, Miyashita Y, Sakai A, Koga N, Kuriyama N, Nonaka K, Fujimoto Y, Jogo T, Hokonohara K, Hu Q, Hisamatsu Y, Ando K, Kimura Y, Yoshizumi T, Mori M. Radiomics texture analysis for the identification of colorectal liver metastases sensitive to first-Line oxaliplatin-based chemotherapy. Ann Surg Oncol. 2021;28:2975–85.

    Article  Google Scholar 

  43. van Helden EJ, Vacher YJL, van Wieringen WN, van Velden FHP, Verheul HMW, Hoekstra OS, Boellaard R, Menke-van der Houven van Oordt CW. Radiomics analysis of pre-treatment [18F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment. Eur J Nucl Med Mol Imaging. 2018;45:2307–2317.

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Funding

The present study was supported by the AIRC (Italian Association for Cancer Research) [grant #2019–23822] (PI: Luca Viganò).

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Author notes

  1. Francesco Fiz and Chiara Masci contributed equally to this work.

Authors and Affiliations

  1. Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy

    Francesco Fiz, Martina Sollini & Arturo Chiti

  2. MOX Laboratory, Department of Mathematics, Politecnico Di Milano, Milan, Italy

    Chiara Masci & Francesca Ieva

  3. Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy

    Guido Costa, Guido Torzilli & Luca Viganò

  4. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4 Pieve Emanuele, 20072, Milan, Italy

    Guido Costa, Martina Sollini, Arturo Chiti, Guido Torzilli & Luca Viganò

  5. CHDS - Center for Health Data Science, Human Technopole, Milan, Italy

    Francesca Ieva

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  1. Francesco Fiz
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by FF, CM, GC, MS, FI, and LV. The first draft of the manuscript was written by FF, CM, GC, MS, and LV, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Luca Viganò.

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Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Humanitas Clinical and Research Hospital (date 23/02/2021: protocol number 5/21).

Informed consent

Because of the retrospective nature of the analysis, the need for informed consent was waived.

Competing interests

The authors declare no competing interests. Considering the conflicts of interest in general, we state that (1) L.V. received speaker’s honoraria from Johnson & Johnson; (2) A.C. received speaker’s honoraria from Advanced Accelerator Applications, General Electric Healthcare, Sirtex Medical Europe, and AmGen Europe; received travel grants from General Electric Healthcare and Sirtex Medical Europe; is a member of Blue Earth Diagnostics’ and Advanced Accelerator Applications’ advisory boards; and received scientific support, in terms of a 3-year Ph.D. fellowship, from the Sanofi Genzyme; (3) F.F. has been a consultant for the MSD Sharp & Dohme GmbH (LLC).

The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

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Fiz, F., Masci, C., Costa, G. et al. PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival. Eur J Nucl Med Mol Imaging 49, 3387–3400 (2022). https://doi.org/10.1007/s00259-022-05765-1

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