Ali Morshid1, Khaled M Elsayes2, Ahmed M Khalaf1, Mohab M Elmohr1, Justin Yu1, Ahmed O Kaseb3, Manal Hassan3, Armeen Mahvash4, Zhihui Wang5, John D Hazle1, David Fuentes1. 1. Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 2. Departments of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 3. Departments of Gastrointestinal Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 4. Departments of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 5. Departments of Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Abstract
PURPOSE: Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. MATERIALS AND METHODS: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control. RESULTS: The model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. CONCLUSION: This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.
PURPOSE: Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. MATERIALS AND METHODS: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control. RESULTS: The model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. CONCLUSION: This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.
Authors: S M Pomerantz; C S White; T L Krebs; B Daly; S A Sukumar; F Hooper; E L Siegel Journal: AJR Am J Roentgenol Date: 2000-02 Impact factor: 3.959
Authors: Yun Ku Cho; Jin Wook Chung; Jae Kyun Kim; Yong Sik Ahn; Mi Young Kim; Yoon Ok Park; Wan Tae Kim; Jong Hoon Byun Journal: Cancer Date: 2008-01-15 Impact factor: 6.860
Authors: Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701
Authors: Shin Ok Jeong; Eui Bae Kim; Soung Won Jeong; Jae Young Jang; Sae Hwan Lee; Sang Gyune Kim; Sang Woo Cha; Young Seok Kim; Young Deok Cho; Hong Soo Kim; Boo Sung Kim; Yong Jae Kim; Dong Erk Goo; Su Yeon Park Journal: Gut Liver Date: 2017-05-15 Impact factor: 4.519
Authors: Cem Simsek; Deniz Can Guven; Taha Koray Sahin; Ibrahim Emir Tekin; Ozlem Sahan; Hatice Yasemin Balaban; Suayib Yalcin Journal: Hepatol Forum Date: 2021-05-21
Authors: Ahmed W Moawad; David Fuentes; Ahmed M Khalaf; Katherine J Blair; Janio Szklaruk; Aliya Qayyum; John D Hazle; Khaled M Elsayes Journal: Front Oncol Date: 2020-05-07 Impact factor: 6.244