PURPOSE: Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS: This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). RESULTS: The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. CONCLUSION: Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.
PURPOSE: Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS: This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). RESULTS: The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. CONCLUSION: Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.
Authors: J R T Monson; M R Weiser; W D Buie; G J Chang; J F Rafferty; W Donald Buie; Janice Rafferty Journal: Dis Colon Rectum Date: 2013-05 Impact factor: 4.585
Authors: Al B Benson; Alan P Venook; Mahmoud M Al-Hawary; Mustafa A Arain; Yi-Jen Chen; Kristen K Ciombor; Stacey Cohen; Harry S Cooper; Dustin Deming; Ignacio Garrido-Laguna; Jean L Grem; Andrew Gunn; Sarah Hoffe; Joleen Hubbard; Steven Hunt; Natalie Kirilcuk; Smitha Krishnamurthi; Wells A Messersmith; Jeffrey Meyerhardt; Eric D Miller; Mary F Mulcahy; Steven Nurkin; Michael J Overman; Aparna Parikh; Hitendra Patel; Katrina Pedersen; Leonard Saltz; Charles Schneider; David Shibata; John M Skibber; Constantinos T Sofocleous; Elena M Stoffel; Eden Stotsky-Himelfarb; Christopher G Willett; Alyse Johnson-Chilla; Lisa A Gurski Journal: J Natl Compr Canc Netw Date: 2020-07 Impact factor: 11.908
Authors: M Bushati; S Pucciarelli; N Gennaro; I Maretto; P Toppan; A Perin; E D L Urso; A Bagatella; G Spolverato Journal: Int J Colorectal Dis Date: 2019-11-14 Impact factor: 2.571
Authors: Maxime J M van der Valk; Denise E Hilling; Esther Bastiaannet; Elma Meershoek-Klein Kranenbarg; Geerard L Beets; Nuno L Figueiredo; Angelita Habr-Gama; Rodrigo O Perez; Andrew G Renehan; Cornelis J H van de Velde Journal: Lancet Date: 2018-06-23 Impact factor: 79.321