Comprehensive Evaluation of the m6A Regulator Prognostic Risk Score in the Prediction of Immunotherapy Response in Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is known for its high drug resistance. The tumor-immune crosstalk mediated by the epigenetic regulation of N6-methyladenosine (m6A) modification has been demonstrated in recent studies. Therefore, m6A modification-mediated immune cell infiltration characteristics may be helpful to guide immunotherapy for ccRCC. Methods: This study comprehensively analyzed m6A modifications using the clinical parameters, single-cell RNA sequencing data, and bulk RNA sequencing data from the TCGA-ccRC cohort and 13 external validation cohorts. A series of bioinformatic approaches were applied to construct an m6A regulator prognostic risk score (MRPRS) to predict survival and immunotherapy response in ccRCC patients. Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low-MRPRS groups. Results: The expressional alteration landscape of m6A regulators was profiled in ccRCC cell clusters and tissue. The 8 regulator genes with minimal lambda were integrated to build an MRPRS, and it was positively correlated with immunotherapeutic response in extent validation cohorts. The clinicopathological features and immune infiltration characteristics could be distinguished by the high- and low-MRPRS. Moreover, the MRPRS-mediated mutation pattern has an enhanced response to immune checkpoint blockade in the ccRCC and pan-cancer cohorts. Conclusions: The proposed MRPRS is a promising biomarker to predict clinical outcomes and therapeutic responses in ccRCC patients.

Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy.

PURPOSE: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm. METHODS: A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases. RESULTS: Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful. CONCLUSION: The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.

Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.

PURPOSE: Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. ACQUISITION AND VALIDATION METHODS: The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. DATA FORMAT AND USAGE NOTES: The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. POTENTIAL APPLICATIONS: This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.