CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

Raoul Santiago; Johanna Ortiz Jimenez; Reza Forghani; Nikesh Muthukrishnan; Olivier Del Corpo; Shairabi Karthigesu; Muhammad Yahya Haider; Caroline Reinhold; Sarit Assouline

doi:10.1016/j.tranon.2021.101188

CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

Transl Oncol. 2021 Oct;14(10):101188. doi: 10.1016/j.tranon.2021.101188. Epub 2021 Jul 31.

Authors

Raoul Santiago¹, Johanna Ortiz Jimenez², Reza Forghani³, Nikesh Muthukrishnan⁴, Olivier Del Corpo⁵, Shairabi Karthigesu⁵, Muhammad Yahya Haider⁵, Caroline Reinhold⁶, Sarit Assouline¹

Affiliations

¹ Jewish General Hospital - McGill University, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada.
² Jewish General Hospital - McGill University, Canada.
³ Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL) of the Department of Radiology and the Research Institute of McGill University Health Centre, Canada; Gerald Bronfman Department of Oncology, Canada; McGill University, Canada. Electronic address: reza.forghani@mcgill.ca.
⁴ Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL) of the Department of Radiology and the Research Institute of McGill University Health Centre, Canada.
⁵ McGill University, Canada.
⁶ Augmented Intelligence & Precision Health Laboratory (AIPHL) of the Department of Radiology and the Research Institute of McGill University Health Centre, Canada; McGill University, Canada.

Abstract

Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.

Keywords: Biomarkers; Diffuse Large B-cell Lymphoma; Quantitative imaging; Radiomics; Refractory.