Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy forHER2 (ERBB2)–Positive Breast Cancer

Abstract

Key Points Question Can quantitative imaging features extracted from the tumor and tumor environment on breast magnetic resonance imaging characterize tumor biological features relevant to outcome of targeted therapy? Findings In this diagnostic study of 209 patients, among HER2 ( ERBB2 )-positive breast cancers, an intratumoral and peritumoral imaging signature capable of discriminating the response-associated HER2 -enriched molecular subtype was identified. When evaluated among recipients of HER2 -targeted therapy, this signature was found to be associated with response to neoadjuvant chemotherapy. Meaning Quantitative analysis of the tumor and its surroundings may provide valuable cues into breast cancer biological features and likelihood of response to targeted therapy.

Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma

“…There is emerging evidence that predictive models should not be limited to mere tumor areas. Recent studies 24 , 25 , 26 , 37 , 38 , 39 , 40 have shown that the surrounding regions may provide complementary information on tumor heterogeneity in other cancers. Here we proposed a noninvasive, CT-based radiomics model with favorable predictive value using both intratumoral and peritumoral radiomics features to predict the possibility of pCR in patients with ESCC before receiving nCRT.…”

Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study

“…Radiomic feature selection was implemented via a two-stage process, with methodological choices based on previous large-scale comparisons of feature selection schemes [54,55]. First, a combination of significance testing and correlation testing was implemented [54] to individually prune F T and F S in order to remove potentially redundant features (whose correlation coefficient was >0.6 [56]). The resulting pruned feature sets were denoted F T and F S for texture and shape features, respectively.…”

Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

“…These novel techniques can determine immune responses such as macrophage activation and lymphocyte infiltration, which has particular relevance in IO, where different lesions can have different TMEs resulting in heterogeneous response patterns. These radiomic signatures have shown promise in multiple tumour types, demonstrating both prognostic and predictive features 152–155 . Leiserson et al developed a multifactorial model for response to ICIs, utilizing an ML method that automatically selects informative features from imported data, and incorporates clinical, tumour, and immunologic features into its model to simultaneously predict clinical benefit 156 .…”