BACKGROUND: Currently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival. METHODS: Patients who underwent resection of stage II/III colon cancer from 2004 to 2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups. RESULTS: The cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases. CONCLUSIONS: CT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.
BACKGROUND: Currently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival. METHODS: Patients who underwent resection of stage II/III colon cancer from 2004 to 2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups. RESULTS: The cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases. CONCLUSIONS: CT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.
Authors: Jian Zheng; Jayasree Chakraborty; William C Chapman; Scott Gerst; Mithat Gonen; Linda M Pak; William R Jarnagin; Ronald P DeMatteo; Richard K G Do; Amber L Simpson Journal: J Am Coll Surg Date: 2017-09-21 Impact factor: 6.113
Authors: Erin S O'Connor; David Yu Greenblatt; Noelle K LoConte; Ronald E Gangnon; Jinn-Ing Liou; Charles P Heise; Maureen A Smith Journal: J Clin Oncol Date: 2011-07-25 Impact factor: 44.544
Authors: Al B Benson; Deborah Schrag; Mark R Somerfield; Alfred M Cohen; Alvaro T Figueredo; Patrick J Flynn; Monika K Krzyzanowska; Jean Maroun; Pamela McAllister; Eric Van Cutsem; Melissa Brouwers; Manya Charette; Daniel G Haller Journal: J Clin Oncol Date: 2004-06-15 Impact factor: 44.544
Authors: Manish A Shah; Lindsay A Renfro; Carmen J Allegra; Thierry André; Aimery de Gramont; Hans-Joachim Schmoll; Daniel G Haller; Steven R Alberts; Greg Yothers; Daniel J Sargent Journal: J Clin Oncol Date: 2016-01-25 Impact factor: 44.544
Authors: Joshua S Jolissaint; Tiegong Wang; Kevin C Soares; Joanne F Chou; Mithat Gönen; Linda M Pak; Thomas Boerner; Richard K G Do; Vinod P Balachandran; Michael I D'Angelica; Jeffrey A Drebin; T P Kingham; Alice C Wei; William R Jarnagin; Jayasree Chakraborty Journal: HPB (Oxford) Date: 2022-02-17 Impact factor: 3.842
Authors: Francesco Fiz; Guido Costa; Nicolò Gennaro; Ludovico la Bella; Alexandra Boichuk; Martina Sollini; Letterio S Politi; Luca Balzarini; Guido Torzilli; Arturo Chiti; Luca Viganò Journal: Diagnostics (Basel) Date: 2021-06-25