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Objectives To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1–2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3–4) and low TNM stage (stages I–II) ccRCC from high TNM stage (stages III–IV). Methods A total of 587 subjects (mean age 60.2 years ± 12.2; range 22–88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). Results The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62–0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74–0.86). Comparable AUCs of 0.73 (95% CI 0.65–0.8) and 0.77 (95% CI 0.7–0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation–based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. Conclusion Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Summary statement Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. Key Points • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62–0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74–0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65–0.80) and 0.77 (95% CI 0.70–0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively .

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care. Plain language summary Artificial intelligence in ulcerative colitis Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon. The clinical care of patients with UC and research efforts to better understand the disease has inevitably produced a significant quantity of diverse and complex datasets ranging from electronic health records, laboratory values, images (endoscopy, radiology, histology) to gene expression. The size and complexity of datasets derived from UC poses a significant challenge to accurately and effectively predict clinically meaningful endpoints in order to ultimately improve UC outcomes. Artificial intelligence through the application of machine learning tools has the potential to improve the analysis of large, complex, high-dimensional datasets and reveal novel, deeper insights compared to traditional analytical tools. Here, we provide an updated and comprehensive summary of AI applications in UC.

Objective To determine the intra‐, inter‐ and test‐retest variability of CT‐based texture analysis (CTTA) metrics. Materials and methods In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra‐scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post‐reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test‐retest) and robustness (intra‐scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter‐scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust. Results As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform‐based texture metrics was overall most reliable across the two scanners and scanning conditions. Post‐processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used. Conclusion Following large‐scale validation, identification of reliable CTTA metrics can aid in conducting large‐scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

BackgroundCraniopharyngiomas are uncommon benign sellar and parasellar tumors with high overall survival (OS) and recurrence rates. Treatment is often surgical but may include adjuvant therapies. The impact of adjuvant therapy and surgical approach have been evaluated, however, facility volume and type have not. The purpose of this study is to analyze the influence of facility volume and type on treatment modalities, extent of surgery and survival of craniopharyngioma.MethodsThe 2004–2016 National Cancer Database (NCDB) was queried for patients diagnosed with craniopharyngioma. Facilities were classified by type (academic vs. non-academic) and low-volume center (LVC) (Treating < 8 patients over the timeline) versus high-volume center (HVC), (Treating ≥ 8 patients over the timeline). Differences in treatment course, outcomes, and OS by facility type were assessed.Results3730 patients (51.3% female) with mean age 41.2 ± 22.0 were included with a 5-year estimated OS of 94.8% (94.0–95.5%). 2564 (68.7%) patients were treated at HVC, of which 2142 (83.5%) were treated at academic facilities. Patients treated at HVC’s were more likely to undergo both surgery and radiation. Surgical approach at HVC was more likely to be endoscopic. Patients treated at HVC demonstrated significantly higher 5-year OS compared to patients treated at LVC (96% [95% CI 95.6–97.1% versus 91.2% [95% CI 89–92.7%] with lower risk of mortality (Hazard ratio [95% CI] = 0.69 [0.56–0.84]).ConclusionTreatment of craniopharyngioma at HVC compared to LVC is associated with improved OS, lower 30- and 90-day postoperative mortality risk, and more common use of both radiotherapy and endoscopic surgical approach.

Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.Analysis of fully clinically annotated and sequenced melanoma tumor samples collected before anti-PD1 treatment suggests that determinants of response differ on the basis of previous anti-CTLA4 therapy, and that tumor mutational burden may not be a strong predictor of response across melanoma subtypes.

The human DF3/MUC1 mucin-like glycoprotein is aberrantly overexpressed by most carcinomas of the breast and other epithelia. The contribution of MUC1 overexpression to the malignant phenotype is, however, not known. In the present studies, we have stably expressed MUC1 in rat 3Y1 fibroblasts. MUC1-positive cells were selected from independent transfections. The results demonstrate that, as found in human carcinomas, MUC1 is expressed on the cell surface and as a complex with β-catenin in the nucleus of the transfectants. Colony formation in soft agar demonstrates that cells expressing MUC1, but not the empty vector, exhibit anchorage-independent growth. The results also show that MUC1 expression confers tumor formation in nude mice. These findings provide the first evidence that MUC1 induces cellular transformation.

Understanding tumor resistance to T cell immunotherapies is critical to improve patient outcomes. Our study revealed a role for transcriptional suppression of the tumor-intrinsic HLA class I (HLA-I) antigen processing and presentation machinery (APM) in therapy resistance. Low HLA-I APM mRNA levels in melanoma metastases before immune checkpoint blockade (ICB) correlated with nonresponsiveness to therapy and poor clinical outcome. Patient-derived melanoma cells with silenced HLA-I APM escaped recognition by autologous CD8+ T cells. However, targeted activation of the innate immunoreceptor RIG-I initiated de novo HLA-I APM transcription, thereby overcoming T cell resistance. Antigen presentation was restored in interferon-sensitive (IFN-sensitive) but also immunoedited IFN-resistant melanoma models through RIG-I-dependent stimulation of an IFN-independent salvage pathway involving IRF1 and IRF3. Likewise, enhanced HLA-I APM expression was detected in RIG-Ihi (DDX58hi) melanoma biopsies, correlating with improved patient survival. Induction of HLA-I APM by RIG-I synergized with antibodies blocking PD-1 and TIGIT inhibitory checkpoints in boosting the antitumor T cell activity of ICB nonresponders. Overall, the herein-identified IFN-independent effect of RIG-I on tumor antigen presentation and T cell recognition proposes innate immunoreceptor targeting as a strategy to overcome intrinsic T cell resistance of IFN-sensitive and IFN-resistant melanomas and improve clinical outcomes in immunotherapy.

Resistance to oncogene-targeted therapies involves discrete drug-tolerant persister cells, originally discovered through in vitro assays. Whether a similar phenomenon limits efficacy of programmed cell death 1 (PD-1) blockade is poorly understood. Here, we performed dynamic single-cell RNA-Seq of murine organotypic tumor spheroids undergoing PD-1 blockade, identifying a discrete subpopulation of immunotherapy persister cells (IPCs) that resisted CD8+ T cell-mediated killing. These cells expressed Snai1 and stem cell antigen 1 (Sca-1) and exhibited hybrid epithelial-mesenchymal features characteristic of a stem cell-like state. IPCs were expanded by IL-6 but were vulnerable to TNF-α-induced cytotoxicity, relying on baculoviral IAP repeat-containing protein 2 (Birc2) and Birc3 as survival factors. Combining PD-1 blockade with Birc2/3 antagonism in mice reduced IPCs and enhanced tumor cell killing in vivo, resulting in durable responsiveness that matched TNF cytotoxicity thresholds in vitro. Together, these data demonstrate the power of high-resolution functional ex vivo profiling to uncover fundamental mechanisms of immune escape from durable anti-PD-1 responses, while identifying IPCs as a cancer cell subpopulation targetable by specific therapeutic combinations.

The purpose of this study is to analyze outcomes of combined antegrade–retrograde dilations (CARD). This retrospective study was conducted on 14 patients with a history of head and neck cancer, treated with radiation therapy that was complicated by either complete or near-complete esophageal stenosis. All patients had minimal oral intake and depended on a gastrostomy tube for nutrition. Swallow function before and after CARD was assessed using the Functional Oral Intake Scale, originally developed for stroke patients and applied to head and neck cancer patients. Patients undergoing CARD demonstrated a quantifiable improvement in swallow function (p = 0.007) that persisted at last known follow-up (p = 0.015) but only a minority (23.1%) achieved oral intake sufficient to obviate the need for tube feeds. Complication rates were 24% per procedure or 36% per patient, almost all complications required procedural intervention, and all complications occurred in patients with complete stenosis. Our study suggests further caution when considering CARD, careful patient selection, and close post-operative monitoring.