Deep learning single-shot detector algorithms identified brain metastases at postcontrast MRI with high sensitivity and moderate positive predictive value, potentially assisting treatment planning ... Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigat...

Background: A strong dose-volume relationship exists between the amount of small bowel receiving low- to intermediate-doses of radiation and the rates of acute, severe gastrointestinal toxicity, principally diarrhea. There is considerable interest in the application of highly conformal treatment approaches, such as intensity-modulated radiation therapy (IMRT), to reduce dose to adjacent organs-at-risk in the treatment of carcinoma of the rectum. Therefore, we performed a comprehensive dosimetric evaluation of IMRT compared to 3-dimensional conformal radiation therapy (3DCRT) in standard, preoperative treatment for rectal cancer. Methods: Using RTOG consensus anorectal contouring guidelines, treatment volumes were generated for ten patients treated preoperatively at our institution for rectal carcinoma, with IMRT plans compared to plans derived from classic anatomic landmarks, as well as 3DCRT plans treating the RTOG consensus volume. The patients were all T3, were node-negative (N = 1) or node-positive (N = 9), and were planned to a total dose of 45-Gy. Pairwise comparisons were made between IMRT and 3DCRT plans with respect to dose-volume histogram parameters. Results: IMRT plans had superior PTV coverage, dose homogeneity, and conformality in treatment of the gross disease and at-risk nodal volume, in comparison to 3DCRT. Additionally, in comparison to the 3DCRT plans, IMRT achieved a concomitant reduction in doses to the bowel (small bowel mean dose: 18.6-Gy IMRT versus 25.2-Gy 3DCRT; p = 0.005), bladder (V_40Gy: 56.8% IMRT versus 75.4% 3DCRT; p = 0.005), pelvic bones (V_40Gy: 47.0% IMRT versus 56.9% 3DCRT; p = 0.005), and femoral heads (V_40Gy: 3.4% IMRT versus 9.1% 3DCRT; p = 0.005), with an improvement in absolute volumes of small bowel receiving dose levels known to induce clinically-relevant acute toxicity (small bowel V_15Gy: 138-cc IMRT versus 157-cc 3DCRT; p = 0.005). We found that the IMRT treatment volumes were typically larger than that covered by classic bony landmark-derived fields, without incurring penalty with respect to adjacent organs-at-risk. Conclusions: For rectal carcinoma, IMRT, compared to 3DCRT, yielded plans superior with respect to target coverage, homogeneity, and conformality, while lowering dose to adjacent organs-at-risk. This is achieved despite treating larger volumes, raising the possibility of a clinically-relevant improvement in the therapeutic ratio through the use of IMRT with a belly-board apparatus.

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.