Joshua S Jolissaint1, Tiegong Wang2, Kevin C Soares3, Joanne F Chou4, Mithat Gönen4, Linda M Pak1, Thomas Boerner3, Richard K G Do5, Vinod P Balachandran3, Michael I D'Angelica3, Jeffrey A Drebin3, T P Kingham3, Alice C Wei3, William R Jarnagin3, Jayasree Chakraborty6. 1. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA. 2. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Surgery, Cangzhou Central Hospital, Cangzhou, Hebei, China. 3. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 5. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 6. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: chakrabj@mskcc.org.
Abstract
BACKGROUND: Most patients recur after resection of intrahepatic cholangiocarcinoma (IHC). We studied whether machine-learning incorporating radiomics and tumor size could predict intrahepatic recurrence within 1-year. METHODS: This was a retrospective analysis of patients with IHC resected between 2000 and 2017 who had evaluable computed tomography imaging. Texture features (TFs) were extracted from the liver, tumor, and future liver remnant (FLR). Random forest classification using training (70.3%) and validation cohorts (29.7%) was used to design a predictive model. RESULTS: 138 patients were included for analysis. Patients with early recurrence had a larger tumor size (7.25 cm [IQR 5.2-8.9] vs. 5.3 cm [IQR 4.0-7.2], P = 0.011) and a higher rate of lymph node metastasis (28.6% vs. 11.6%, P = 0.041), but were not more likely to have multifocal disease (21.4% vs. 17.4%, P = 0.643). Three TFs from the tumor, FD1, FD30, and IH4 and one from the FLR, ACM15, were identified by feature selection. Incorporation of TFs and tumor size achieved the highest AUC of 0.84 (95% CI 0.73-0.95) in predicting recurrence in the validation cohort. CONCLUSION: This study demonstrates that radiomics and machine-learning can reliably predict patients at risk for early intrahepatic recurrence with good discrimination accuracy.
BACKGROUND: Most patients recur after resection of intrahepatic cholangiocarcinoma (IHC). We studied whether machine-learning incorporating radiomics and tumor size could predict intrahepatic recurrence within 1-year. METHODS: This was a retrospective analysis of patients with IHC resected between 2000 and 2017 who had evaluable computed tomography imaging. Texture features (TFs) were extracted from the liver, tumor, and future liver remnant (FLR). Random forest classification using training (70.3%) and validation cohorts (29.7%) was used to design a predictive model. RESULTS: 138 patients were included for analysis. Patients with early recurrence had a larger tumor size (7.25 cm [IQR 5.2-8.9] vs. 5.3 cm [IQR 4.0-7.2], P = 0.011) and a higher rate of lymph node metastasis (28.6% vs. 11.6%, P = 0.041), but were not more likely to have multifocal disease (21.4% vs. 17.4%, P = 0.643). Three TFs from the tumor, FD1, FD30, and IH4 and one from the FLR, ACM15, were identified by feature selection. Incorporation of TFs and tumor size achieved the highest AUC of 0.84 (95% CI 0.73-0.95) in predicting recurrence in the validation cohort. CONCLUSION: This study demonstrates that radiomics and machine-learning can reliably predict patients at risk for early intrahepatic recurrence with good discrimination accuracy.
Authors: Emily A Aherne; Linda M Pak; Debra A Goldman; Mithat Gonen; William R Jarnagin; Amber L Simpson; Richard K Do Journal: Abdom Radiol (NY) Date: 2018-10
Authors: Fabio Bagante; Gaya Spolverato; Katiuscha Merath; Matthew Weiss; Sorin Alexandrescu; Hugo P Marques; Luca Aldrighetti; Shishir K Maithel; Carlo Pulitano; Todd W Bauer; Feng Shen; George A Poultsides; Olivier Soubrane; Guillaume Martel; B Groot Koerkamp; Alfredo Guglielmi; Itaru Endo; Timothy M Pawlik Journal: Surgery Date: 2019-07-17 Impact factor: 3.982
Authors: Angela Lamarca; Paul Ross; Harpreet S Wasan; Richard A Hubner; Mairéad G McNamara; Andre Lopes; Prakash Manoharan; Daniel Palmer; John Bridgewater; Juan W Valle Journal: J Natl Cancer Inst Date: 2020-02-01 Impact factor: 13.506
Authors: Mariam Jamal-Hanjani; Gareth A Wilson; Nicholas McGranahan; Nicolai J Birkbak; Thomas B K Watkins; Selvaraju Veeriah; Seema Shafi; Diana H Johnson; Richard Mitter; Rachel Rosenthal; Max Salm; Stuart Horswell; Mickael Escudero; Nik Matthews; Andrew Rowan; Tim Chambers; David A Moore; Samra Turajlic; Hang Xu; Siow-Ming Lee; Martin D Forster; Tanya Ahmad; Crispin T Hiley; Christopher Abbosh; Mary Falzon; Elaine Borg; Teresa Marafioti; David Lawrence; Martin Hayward; Shyam Kolvekar; Nikolaos Panagiotopoulos; Sam M Janes; Ricky Thakrar; Asia Ahmed; Fiona Blackhall; Yvonne Summers; Rajesh Shah; Leena Joseph; Anne M Quinn; Phil A Crosbie; Babu Naidu; Gary Middleton; Gerald Langman; Simon Trotter; Marianne Nicolson; Hardy Remmen; Keith Kerr; Mahendran Chetty; Lesley Gomersall; Dean A Fennell; Apostolos Nakas; Sridhar Rathinam; Girija Anand; Sajid Khan; Peter Russell; Veni Ezhil; Babikir Ismail; Melanie Irvin-Sellers; Vineet Prakash; Jason F Lester; Malgorzata Kornaszewska; Richard Attanoos; Haydn Adams; Helen Davies; Stefan Dentro; Philippe Taniere; Brendan O'Sullivan; Helen L Lowe; John A Hartley; Natasha Iles; Harriet Bell; Yenting Ngai; Jacqui A Shaw; Javier Herrero; Zoltan Szallasi; Roland F Schwarz; Aengus Stewart; Sergio A Quezada; John Le Quesne; Peter Van Loo; Caroline Dive; Allan Hackshaw; Charles Swanton Journal: N Engl J Med Date: 2017-04-26 Impact factor: 91.245