MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with locally-advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHOD: Fifty-five consecutive patients with LARC were retrospectively enrolled in this study. Patients underwent 3 T Magnetic Resonance Imaging (MRI) acquiring T2-weighted images before, during and after CRT. All patients underwent complete surgical resection and histopathology was the gold standard. Textural features were automatically extracted using an open-source software. A sub-set of statistically significant textural features was selected and two AI models were built by training a Random Forest (RF) classifier on 28 patients (training cohort). Model performances were estimated on 27 patients (validation cohort) using a ROC curve and a decision curve analysis. RESULTS: Sixteen of 55 patients achieved CR. The AI model for CR classification showed good discrimination power with mean area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.70, 0.94) in the validation cohort. The discriminatory power for the NR classification showed a mean AUC of 0.83 (95% CI: 0.71,0.92). Decision curve analysis confirmed higher net patient benefit when using AI models compared to standard-of-care. CONCLUSIONS: AI models based on textural features of MR images of patients with LARC may help to identify patients who will show CR at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.

Observation of multiple volume reflection of ultrarelativistic protons by a sequence of several bent silicon crystals.

The interactions of 400 GeV protons with different sequences of bent silicon crystals have been investigated at the H8 beam line of the CERN Super Proton Synchrotron. The multiple volume reflection of the proton beam has been studied in detail on a five-crystal reflector measuring an angular beam deflection theta = 52.96 +/- 0.14 microrad. The efficiency was found larger than 80% for an angular acceptance at the reflector entrance of 70 microrad, with a maximal efficiency value of epsilon = 0.90 +/- 0.01 +/- 0.03.

Volume reflection dependence of 400 GeV/c protons on the bent crystal curvature.

The trend of volume reflection parameters (deflection angle and efficiency) in a bent (110) silicon crystal has been investigated as a function of the crystal curvature with 400 GeV/c protons on the H8 beam line at the CERN Super Proton Synchrotron. This Letter describes the analysis performed at six different curvatures showing that the optimal radius for volume reflection is approximately 10 times greater than the critical radius for channeling. A strong scattering of the beam by the planar potential is also observed for a bend radius close to the critical one.