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Purpose: Extramural venous invasion (EMVI) is recognized as a poor prognostic factor in rectal cancer. There are well-documented limitations associated with pathology detection of EMVI, including variable reporting and the inability to use it preoperatively to guide neoadjuvant treatment. Magnetic resonance imaging (MRI)-detected EMVI (mrEMVI) has been proposed as an imaging biomarker. This review assesses the prognostic significance of mrEMVI on survival outcomes and whether regression of mrEMVI after neoadjuvant therapy is associated with improvements in survival.Methods and Materials: An electronic search was carried out using MEDLINE and EMBASE databases using the search terms "rectum," "cancer,", "MRI," and "outcomes." A systematic review and meta-analysis were carried out in accordance with Preferred Reporting for Systematic Reviews and Meta-Analyses guidelines using Review Manager software. A qualitative review was performed.Results: A total of 7399 articles were identified, of which 33 were relevant to the review question. After a qualitative assessment, 20 articles were included in the meta-analysis. Baseline mrEMVI positivity is associated with significantly worsened overall survival (hazard ratio [HR] 1.84; 95% confidence interval [CI], 1.33-2.54; P = .0001) and significantly worsened disease-free survival (HR 2.41; 95% CI, 2.02-2.89; P < .00001). After neoadjuvant treatment, a positive mrEMVI status is associated with a significantly worsened overall and disease-free survival. Only 3 papers specifically looked at mrEMVI regression, but the results show that persistent mrEMVI-positive status after treatment is associated with significantly worsened disease-free survival compared with a change in mrEMVI from positive to negative (HR 1.93; 95% CI, 1.39-2.68; P < .0001). A subgroup analysis of MRI-detected lymph node metastases showed no significant association with survival, with a hazard ratio of 1.33 (95% CI, 0.98-1.80; P = .06).Conclusion: mrEMVI is significantly associated with worsened survival outcomes, both at baseline and after neoadjuvant treatment. Additionally, there is evidence that regression of mrEMVI after neoadjuvant treatment is associated with improved survival compared with mrEMVI persistence. The findings of this review emphasize the need for accurate and consistent reporting of mrEMVI status before and after neoadjuvant treatment and support the inclusion of mrEMVI into staging systems preferentially over lymph node metastases. [ABSTRACT FROM AUTHOR]

Purpose: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images.Methods and Materials: This paper presents a retrospective analysis of the pulmonary technetium-99m-labeled macroaggregated albumin single-photon emission CT (SPECT)/CT scans obtained from 73 patients at Queen Mary Hospital in Hong Kong in 2019. The left and right lung scans were separated to double the size of the data set to 146. A 3-dimensional attention residual neural network was constructed to extract textural features from the CT images and reconstruct corresponding functional images. Eighty-four samples were randomly selected for training and cross-validation, and the remaining 62 were used for model testing in terms of voxel-wise agreement and function-wise concordance. To assess the voxel-wise agreement, the Spearman's correlation coefficient (R) and structural similarity index measure between the images predicted by the DL-CTPM and the corresponding SPECT perfusion images were computed to assess the statistical and perceptual image similarities, respectively. To assess the function-wise concordance, the Dice similarity coefficient (DSC) was computed to determine the similarity of the low/high functional lung volumes.Results: The evaluation of the voxel-wise agreement showed a moderate-to-high voxel value correlation (0.6733 ± 0.1728) and high structural similarity (0.7635 ± 0.0697) between the SPECT and DL-CTPM predicted perfusions. The evaluation of the function-wise concordance obtained an average DSC value of 0.8183 ± 0.0752 for high-functional lungs (range, 0.5819-0.9255) and 0.6501 ± 0.1061 for low-functional lungs (range, 0.2405-0.8212). Ninety-four percent of the test cases demonstrated high concordance (DSC >0.7) between the high-functional volumes contoured from the predicted and ground-truth perfusions.Conclusions: We developed a novel DL-CTPM method for estimating perfusion-based lung functional images from the CT domain using a 3-dimensional attention residual neural network, which yielded moderate-to-high voxel-wise approximations of lung perfusion. To further contextualize these results toward future clinical application, a multi-institutional large-cohort study is warranted. [ABSTRACT FROM AUTHOR]