Prediction of Pathologic Complete Response for Rectal Cancer Based on Pretreatment Factors Using Machine Learning
Creators: name: Goffredo, Paolo
name: Butler, Logan R.
name: Gomez, Shawn M.
name: Joisa, Chinmaya U.
name: Guillem, Jose G.
name: Chen, Kevin A.
name: Kapadia, Muneera R.
Publisher: name: Wolters Kluwer
Year: 2023

description: <p>BACKGROUND: Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated nonoperatively in the future. Existing models for predicting pathologic complete response in the pretreatment setting are limited by small data sets and low accuracy. OBJECTIVE: We sought to use machine learning to develop a more generalizable predictive model for pathologic complete response for locally advanced rectal cancer. DESIGN: Patients with locally advanced rectal cancer who underwent neoadjuvant therapy followed by surgical resection were identified in the National Cancer Database from years 2010 to 2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using an area under the receiver operating characteristic curve. SETTINGS: This study used a national, multicenter data set. PATIENTS: Patients with locally advanced rectal cancer who underwent neoadjuvant therapy and proctectomy. MAIN OUTCOME MEASURES: Pathologic complete response defined as T0/xN0/x. RESULTS: The data set included 53,684 patients. Pathologic complete response was experienced by 22.9% of patients. Gradient boosting showed the best performance with an area under the receiver operating characteristic curve of 0.777 (95% CI, 0.773-0.781), compared with 0.684 (95% CI, 0.68-0.688) for logistic regression. The strongest predictors of pathologic complete response were no lymphovascular invasion, no perineural invasion, lower CEA, smaller size of tumor, and microsatellite stability. A concise model including the top 5 variables showed preserved performance. LIMITATIONS: The models were not externally validated. CONCLUSIONS: Machine learning techniques can be used to accurately predict pathologic complete response for locally advanced rectal cancer in the pretreatment setting. After fine-tuning a data set including patients treated nonoperatively, these models could help clinicians identify the appropriate candidates for a watch-and-wait strategy. See Video Abstract . EL CNCER DE RECTO BASADA EN FACTORES PREVIOS AL TRATAMIENTO MEDIANTE EL APRENDIZAJE AUTOMTICO: ANTECEDENTES:La respuesta patol&oacute;gica completa despu&eacute;s de la terapia neoadyuvante es un indicador pron&oacute;stico importante para el c&aacute;ncer de recto localmente avanzado y puede dar informaci&oacute;n sobre qu&eacute; pacientes podr&iacute;an ser tratados de forma no quir&uacute;rgica en el futuro. Los modelos existentes para predecir la respuesta patol&oacute;gica completa en el entorno previo al tratamiento est&aacute;n limitados por conjuntos de datos peque&ntilde;os y baja precisi&oacute;n.OBJETIVO:Intentamos utilizar el aprendizaje autom&aacute;tico para desarrollar un modelo predictivo m&aacute;s generalizable para la respuesta patol&oacute;gica completa para el c&aacute;ncer de recto localmente avanzado.DISE&Ntilde;O:Los pacientes con c&aacute;ncer de recto localmente avanzado que se sometieron a terapia neoadyuvante seguida de resecci&oacute;n quir&uacute;rgica se identificaron en la Base de Datos Nacional del C&aacute;ncer de los a&ntilde;os 2010 a 2019 y se dividieron en conjuntos de capacitaci&oacute;n, validaci&oacute;n y prueba. Las t&eacute;cnicas de aprendizaje autom&aacute;tico incluyeron bosque aleatorio, aumento de gradiente y red neuronal artificial. Tambi&eacute;n se cre&oacute; un modelo de regresi&oacute;n log&iacute;stica. El rendimiento del modelo se evalu&oacute; utilizando el &aacute;rea bajo la curva caracter&iacute;stica operativa del receptor.&Aacute;MBITO:Este estudio utiliz&oacute; un conjunto de datos nacional multic&eacute;ntrico.PACIENTES:Pacientes con c&aacute;ncer de recto localmente avanzado sometidos a terapia neoadyuvante y proctectom&iacute;a.PRINCIPALES MEDIDAS DE VALORACI&Oacute;N:Respuesta patol&oacute;gica completa definida como T0/xN0/x.RESULTADOS:El conjunto de datos incluy&oacute; 53.684 pacientes. El 22,9% de los pacientes experimentaron una respuesta patol&oacute;gica completa. El refuerzo de gradiente mostr&oacute; el mejor rendimiento con un &aacute;rea bajo la curva caracter&iacute;stica operativa del receptor de 0,777 (IC del 95%: 0,773 - 0,781), en comparaci&oacute;n con 0,684 (IC del 95%: 0,68 - 0,688) para la regresi&oacute;n log&iacute;stica. Los predictores m&aacute;s fuertes de respuesta patol&oacute;gica completa fueron la ausencia de invasi&oacute;n linfovascular, la ausencia de invasi&oacute;n perineural, un CEA m&aacute;s bajo, un tama&ntilde;o m&aacute;s peque&ntilde;o del tumor y la estabilidad de los microsat&eacute;lites. Un modelo conciso que incluye las cinco variables principales mostr&oacute; un rendimiento preservado.LIMITACIONES:Los modelos no fueron validados externamente.CONCLUSIONES:Las t&eacute;cnicas de aprendizaje autom&aacute;tico se pueden utilizar para predecir con precisi&oacute;n la respuesta patol&oacute;gica completa para el c&aacute;ncer de recto localmente avanzado en el entorno previo al tratamiento. Despu&eacute;s de realizar ajustes en un conjunto de datos que incluye pacientes tratados de forma no quir&uacute;rgica, estos modelos podr&iacute;an ayudar a los m&eacute;dicos a identificar a los candidatos adecuados para una estrategia de observar y esperar. (Traducci&oacute;n-Dr. Ingrid Melo ).</p>