Radiomics predicts response of individual HER2‐amplified colorectal cancer liver metastases in patients treated with HER2‐targeted therapy

Abstract

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R−), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases

“…Prior studies have already proposed the use of radiomics signature for per-lesion analysis in different cancer types, including CRC patients [ 31 , 32 , 33 ]. Giannini et al [ 32 ], for example, validated a radiomics model form baseline CT to predict the behavior of individual lmCRC to targeted treatment in a cohort of HER2 amplified CRC patients. However, results from radiomics analysis performed on baseline CT were sometimes contradictory.…”

Radiogenomics in Hepatocellular Carcinoma: Machine Learning Based CT Texture Analysis in Predicting β-Catenin Mutation Status

“…CT-based radiomics analysis had been used to predict survival of patients with metastatic colorectal cancer [24] . Radiomics could also be used to predict response of individual HER2-ampli ed colorectal cancer liver metastases, as well as the biomarkers of molecular subtype prognosis [25] .…”

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

“…This method was later validated against the multidisciplinary team meeting achieving an almost perfect agreement[ 108 ]. Since then, few studies have been published, but they outlined a progressive increase in AI performances[ 109 - 113 ]. The AI predicted the recurrence risk after surgery by taking into account clinical, pathology, and laboratory data[ 110 , 112 ].…”

“…The AI predicted the recurrence risk after surgery by taking into account clinical, pathology, and laboratory data[ 110 , 112 ]. The addition of radiomic features into the machine learning models further optimized and anticipated the prediction[ 109 , 111 ]. Wei et al [ 113 ] compared a clinical, radiomic, and AI-based model to predict response to first-line chemotherapy; the deep-learning model had the best results, outperforming not only the model based on clinical parameters but also the one including texture analysis.…”