Predicting peripheral neuropathy following neoadjuvant therapy in patients with breast cancer.

Abstract

e12639 Background: Administration of neoadjuvant chemotherapy (NACT) or radiation is widely used to improve outcomes in patients with breast cancer. This strategy has demonstrated remarkable efficacy in reducing tumor burden and increasing the likelihood of breast-conserving surgery. However, neoadjuvant therapy can induce peripheral neuropathy, a condition characterized by nerve damage or dysfunction in the extremities that often manifests as pain, tingling, or weakness in the hands and feet. The exact mechanism remains unclear, although likely involves the cumulative toxic effects of therapy on peripheral nerves. Treatment-induced neuropathy poses a substantial unaddressed challenge that can significantly impact the patient’s quality of life. Therefore, predicting and ameliorating neuropathy are crucial aspects of comprehensive cancer care. Methods: We used an AI spatial biology platform to create and assess 3D depictions of a cohort of breast cancer patients based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Clinical, pathological, and radiological data were collected on 74 patients from Yale New Haven Hospital. Due to missing regimen data, the final dataset included 71 patients, with mean age 51.1 (14.5) years. An AI-based segmentation algorithm was successfully employed on 63 patients, of whom 13 had neuropathy following NACT. Vascular and microvascular (MV/V) kinetic parameters were computed for each, and radiomic features were extracted. Machine learning models were developed for clinical features, MV/V features, and the combined set of features. All models were tested with leave-one-out cross-validation. Results: The clinical model did not forecast neuropathy in any patient, indicating poor predictive ability. The MV/V model performed the best, with accuracy, precision, recall, and AUROC of 0.84, 1.0, 0.18, and 0.69, respectively. The combined model showed intermediate performance, with accuracy, precision, recall, and AUROC of 0.81, 0.5, 0.18, and 0.58, respectively. The patient population limits broad conclusions, but suggests features that are aligned with increased occurrence of neuropathy. In particular, vascular features that describe transfer of nutrients between tissues, as well as the density of blood vessels within and around the tumor, are prominent within both the MV/V and combined models. Conclusions: These results indicate that features associated with the degree of proximal and intratumoral vascularity may be predictive of peripheral neuropathy in breast cancer patients. Moreover, these features can be extracted from standard-of-care DCE-MRI data. Expansion of this vascular analysis could enable physicians to better plan and manage care for patients by tailoring treatment dose. [Table: see text]