A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

Background: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery.

Methods: This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response.

AUTOMATIC DETECTION AND TRACKING OF LONGITUDINAL CHANGES OF MULTIPLE BONE METASTASES FROM DUAL ENERGY CT.

Early detection and the assessment of changes in bone metastatic cancers can enable clinicians to monitor disease progression and modify treatment to help achieve improved results for patients. However, poor contrast makes detection difficult, and multiple disease sites make tracking of their changes over time difficult. We present a method for automatically detecting and tracking the longitudinal changes in multiple sclerotic bone metastases from Dual Energy Computed Tomography (DECT) images.

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs.

Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.

Objectives: To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS).

Methods: One hundred and forty-seven patients underwent T2- weighted (T2WI) and diffusion-weighted prostate MRI. Cancers ≥0.