Internal Cohort Research Articles

Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events

BackgroundThe cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. ObjectivesThis study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. MethodsA total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation–related stroke. ResultsMACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, −17.3% for GCS). ConclusionsML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning

BackgroundThe advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.MethodsWe established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations.ResultsThe models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration.ConclusionsWe proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.Graphical

Next‐generation sequencing identifies that protein tyrosine phosphatase receptor type D mutation is favorable to immunotherapy in human cancer

AbstractProtein tyrosine phosphatase receptors (PTPRs) play a crucial part in numerous tumor processes. However, the effect of PTPR mutations on the immune checkpoint inhibitor (ICI) response needs to be further clarified. Next‐generation sequencing was performed on 453 cancer patients in our internal cohort. The genomic alterations, tumor mutation burden (TMB), neoantigens, and immune‐related features/pathways of other cohorts were analyzed. Here, protein tyrosine phosphatase receptor type D (PTPRD) has a high mutation frequency and an intensified co‐occurrence with other PTPRs. Patients who responded to ICI therapy were enriched with the PTPRD mutation (PTPRD‐MUT). PTPRD‐MUT patients had a higher objective response rate (44.1% vs. 29.1%), TMB/neoantigens, and longer overall survival time than PTPRD‐wild‐type (PTPRD‐WT) patients. Genomic alterations with a higher mutation frequency of genes (such as LRP1B) were enriched in PTPRD‐MUT patients. More abundant immune cells (including CD8+ T cells and macrophages) and upregulated immune‐related genes were found in PTPRD‐MUT patients. Moreover, Gene sets enrichment analyses showed that multiple antitumor immune pathways are activated in PTPRD‐MUT patients. Therefore, PTPRD‐MUT is beneficial for immunotherapy of multiple cancer types and may be a predictive biomarker of patient clinical outcomes.

Prognostic Assessmentin Patients With Primary Diffuse Large B-Cell Lymphoma of the Central Nervous System Using MRI-Based Radiomics.

Primary central nervous system lymphoma (PCNSL) carries a poor prognosis. Radiomics may hold potential value in prognostic assessment. To develop and validate an MRI-based radiomics model and combine it with clinical factors to assess progression-free survival (PFS) and overall survival (OS) of patients with PCNSL. Retrospective and prospective. Three hundred seventy-nine patients (179 female, 53 ± 7 years) from 2014 to 2022. T2/fluid-attenuated inversion recovery, contrast-enhanced T1WI and diffusion-weighted echo-planar imaging sequences on 3.0 T. Radiomics features were extracted from enhanced tumor regions on preoperative multi-sequence MRI. Using a least absolute shrinkage and selection operator (LASSO) Cox regression model to select radiomic signatures in training cohort (N = 169). Cox proportional hazards models were constructed for clinical, radiomics, and combined models, with internal (N = 72) and external (N = 32) cohorts validating model performance. Chi-squared, Mann-Whitney, Kaplan-Meier, log-rank, LASSO, Cox, decision curve analysis, time-dependent Receiver Operating Characteristic, area under the curve (AUC), and likelihood ratio test. P-value <0.05 was considered significant. Follow-up duration was 28.79 ± 22.59 months (median: 25). High-risk patients, determined by the median radiomics score, showed significantly lower survival rates than low-risk patients. Compared with NCCN-IPI, conventional imaging and clinical models, the combined model achieved the highest C-index for both PFS (0.660 internal, 0.802 external) and OS (0.733 internal, 0.781 external) in validation. Net benefit was greater with radiomics than with clinical alone. The combined model exhibited performance with AUCs of 0.680, 0.752, and 0.830 for predicting 1-year, 3-year, and 5-year PFS, and 0.770, 0.789, and 0.863 for OS in internal validation, with PFS AUCs of 0.860 and 0.826 and OS AUCs of 0.859 and 0.748 for 1-year and 3-year survival in external validation. Incorporating a multi-sequence MR-based radiomics model into clinical models enhances the assess accuracy for the prognosis of PCNSL. 4 TECHNICAL EFFICACY: Stage 2.

Interpretable artificial intelligence to optimise use of imatinib after resection in patients with localised gastrointestinal stromal tumours: an observational cohort study

Current guidelines recommend use of adjuvant imatinib therapy for many patients with gastrointestinal stromal tumours (GISTs); however, its optimal treatment duration is unknown and some patient groups do not benefit from the therapy. We aimed to apply state-of-the-art, interpretable artificial intelligence (ie, predictions or prescription logic that can be easily understood) methods on real-world data to establish which groups of patients with GISTs should receive adjuvant imatinib, its optimal treatment duration, and the benefits conferred by this therapy. In this observational cohort study, we considered for inclusion all patients who underwent resection of primary, non-metastatic GISTs at the Memorial Sloan Kettering Cancer Center (MSKCC; New York, NY, USA) between Oct 1, 1982, and Dec 31, 2017, and who were classified as intermediate or high risk according to the Armed Forces Institute of Pathology Miettinen criteria and had complete follow-up data with no missing entries. A counterfactual random forest model, which used predictors of recurrence (mitotic count, tumour size, and tumour site) and imatinib duration to infer the probability of recurrence at 7 years for a given patient under each duration of imatinib treatment, was trained in the MSKCC cohort. Optimal policy trees (OPTs), a state-of-the-art interpretable AI-based method, were used to read the counterfactual random forest model by training a decision tree with the counterfactual predictions. The OPT recommendations were externally validated in two cohorts of patients from Poland (the Polish Clinical GIST Registry), who underwent GIST resection between Dec 1, 1981, and Dec 31, 2011, and from Spain (the Spanish Group for Research in Sarcomas), who underwent resection between Oct 1, 1987, and Jan 30, 2011. Among 1007 patients who underwent GIST surgery in MSKCC, 117 were included in the internal cohort; for the external cohorts, the Polish cohort comprised 363 patients and the Spanish cohort comprised 239 patients. The OPT did not recommend imatinib for patients with GISTs of gastric origin measuring less than 15·9 cm with a mitotic count of less than 11·5 mitoses per 5 mm2 or for those with small GISTs (<5·4 cm) of any site with a count of less than 11·5 mitoses per 5 mm2. In this cohort, the OPT cutoffs had a sensitivity of 92·7% (95% CI 82·4-98·0) and a specificity of 33·9% (22·3-47·0). The application of these cutoffs in the two external cohorts would have spared 38 (29%) of 131 patients in the Spanish cohort and 44 (35%) of 126 patients in the Polish cohort from unnecessary treatment with imatinib. Meanwhile, the risk of undertreating patients in these cohorts was minimal (sensitivity 95·4% [95% CI 89·5-98·5] in the Spanish cohort and 92·4% [88·3-95·4] in the Polish cohort). The OPT tested 33 different durations of imatinib treatment (<5 years) and found that 5 years of treatment conferred the most benefit. If the identified patient subgroups were applied in clinical practice, as many as a third of the current cohort of candidates who do not benefit from adjuvant imatinib would be encouraged to not receive imatinib, subsequently avoiding unnecessary toxicity on patients and financial strain on health-care systems. Our finding that 5 years is the optimal duration of imatinib treatment could be the best source of evidence to inform clinical practice until 2028, when a randomised controlled trial with the same aims is expected to report its findings. National Cancer Institute.

Development of a Novel Prognostic Panel for Colorectal Cancer Based on Cancer Functional Status, and Validation of STC2 as a Promising Biomarker.

Improving the clinical outcome of colorectal cancer (CRC) patients remains a major challenge. This study aimed to develop a new predictive classifier for CRC and to examine its relationship with the immune environment and therapeutic response. A comprehensive bioinformatics analysis was applied to develop a risk panel comprised of cancer function status-related genes (CFSRGs). This panel was evaluated for prognostic utility by Area Under the Curve (AUC) and Kaplan-Meier (KM) analyses. Differences between high- and low-risk groups were subsequently investigated using multi-omics data. Immunohistochemistry (IHC), quantitative real-time polymerase chain reaction (qRT-PCR), and cell phenotype assays were also employed to ascertain the clinical value of STC2 expression. Significant differences were observed in the survival rate between high- and low-risk groups defined by our 7-CFSRG panel, both in internal and external CRC patient cohorts. The AUC for prediction of survival at 1-, 3- and 5-years was satisfactory in all cohorts. Detailed analysis revealed that tumor mutation burden, drug sensitivity, and pathological stage were closely associated with the risk score. Elevated expression of STC2 in CRC tissues relative to normal paraneoplastic tissues was associated with less favorable patient outcomes. qRT-PCR experiments confirmed that STC2 expression was significantly upregulated in several CRC cell lines (HCT116, SW480, and LOVO) compared to a normal intestinal epithelial cell line (NCM460). The proliferation, migration, and invasion of CRC cells were all significantly inhibited by knockdown of STC2. Our 7-CFSRG panel is a promising classifier for assessing the prognosis of CRC patients. Moreover, the targeting of STC2 may provide a novel therapeutic approach for improving patient outcomes.

Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.

Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early prediction of spontaneous pregnancy after reproductive surgery is of high interest for the patients seeking the infertility treatment. However, there are no high-quality models and clinical applicable tools to predict the probability of natural conception after reproductive surgery. The eligible data involving 1013 patients who operated for infertility between June 2016 and June 2021 in Yantai Yuhuangding Hospital in China, were randomly divided into training and internal testing cohorts. 195 subjects from the Linyi People's Hospital in China were considered for external validation. Both univariate combining with multivariate logistic regression and the least absolute shrinkage and selection operator (LASSO) algorithm were performed to identify independent predictors. Multiple common machine learning algorithms, namely logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, and extreme gradient boosting, were employed to construct the predictive models. The optimal model was verified by evaluating the model performance in both the internal and external validation datasets. Six clinical indicators, including female age, infertility type, duration of infertility, intraoperative diagnosis, ovulation monitoring, and anti-Müllerian hormone (AMH) level, were screened out. Based on the logistic regression model's superior clinical predictive value, as indicated by the area under the receiver operating characteristic curve (AUC) in both the internal (0.870) and external (0.880) validation sets, we ultimately selected it as the optimal model. Consequently, we utilized it to generate a web-based nomogram for predicting the probability of spontaneous pregnancy after reproductive surgery. Furthermore, the calibration curve, Hosmer-Lemeshow (H-L) test, the decision curve analysis (DCA) and clinical impact curve analysis (CIC) demonstrated that the model has superior calibration degree, clinical net benefit and generalization ability, which were confirmed by both internal and external validations. Overall, our developed first nomogram with online operation provides an early and accurate prediction for the probability of natural conception after reproductive surgery, which helps clinicians and infertile couples make sensible decision of choosing the mode of subsequent conception, natural or IVF, to further improve the clinical practices of infertility treatment.

CD147 protein molecule expression and chromosomal instability in the pathogenesis and prognosis of thyroid cancer

Low-coverage whole genome sequencing was performed for tissue samples from thyroid patients who received surgery treatment from 2015 to 2021. The potential biological significance of CD147 protein in thyroid cancer was explored through correlation analysis of CD147 protein expression level and clinical features of thyroid cancer patients. Low coverage whole genome sequencing was performed on the extracted DNA samples. The copy number analysis software was used to analyze the sequencing data, calculate the copy number of CD147 gene, further verify the expression of CD147 gene, and analyze its association with clinical features. The relationship between CIN and high risk was evaluated in the internal cohort. The association of CIN with the disease-free survival was validated in the cohort from The Cancer Genome Atlas Program. Thyroglobulin plays a key role in regulating thyroid function and maintaining normal metabolic rate. By sequencing tissue samples from this study, we can gain a deeper understanding of the association between cin and thyroid disease. The percentage of high risk patients in the multiple CIN group (77.8 %) was significantly higher than that in the 22q negative group (31.3 %), BRAF V600E group (22.2 %) and all negative group (25.0 %; p = 0.043).

Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging.

To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images. Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models weretrained with 397 patients and validated with 170 internal and 31 external patients. For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively. Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.

Predicting Acupuncture Efficacy in Chronic Prostatitis/Chronic Pelvic Pain Syndrome: A Study on Model Development and Result Visualization.

Acupuncture is one of primary treatment options for chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), but its efficacy varies among patients. This study aimed to develop and validate a nomogram for predicting the efficacy of acupuncture in CP/CPPS. This study enrolled 220 patients with CP/CPPS who received acupuncture. Patients were divided into a responder group and nonresponder group based on the reduction in the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI). Potential variables were selected using the least absolute shrinkage and selection operator regression, and a nomogram was established using the multivariable logistic regression model. The performance of the nomogram was assessed by the receiver operating characteristic curves and calibration curves. Two Hundred Twenty men were randomly assigned to the training cohort (n = 154) and the internal test cohort (n = 66). The developed nomogram included age, current drinking status, sedentary lifestyle, habit of staying up late, expectations for acupuncture, comorbidities, NIH-CPSI pain subscale and total scores. The area under the curve of the prediction model was 0.777 (95% CI: 0.702-0.851) in the training cohort, 0.752 (95% CI: 0.616-0.888) in the internal test cohort, demonstrating satisfactory discriminative ability as indicated by the calibration curve. The nomogram accurately identified CP/CPPS patients who would benefit from acupuncture. Factors such as youth, abstention from alcohol, avoiding sedentary habits and staying up late, having high expectations for acupuncture, being free from comorbidities, and baseline high scores on both the NIH-CPSI pain subscale and total scores may positively affect the efficacy of acupuncture. Further validation of our findings requires multicenter and large-sample prospective studies.

Dissecting gastric cancer heterogeneity and exploring therapeutic strategies using bulk and single-cell transcriptomic analysis and experimental validation of tumor microenvironment and metabolic interplay.

Gastric cancer, the fifth most prevalent cancer worldwide, is often diagnosed in advanced stages with limited treatment options. Examining the tumor microenvironment (TME) and its metabolic reprogramming can provide insights for better diagnosis and treatment. This study investigates the link between TME factors and metabolic activity in gastric cancer using bulk and single-cell RNA-sequencing data. We identified two molecular subtypes in gastric cancer by analyzing the distinct expression patterns of 81 prognostic genes related to the TME and metabolism, which exhibited significant protein-level interactions. The high-risk subtype had increased stromal content, fibroblast and M2 macrophage infiltration, elevated glycosaminoglycans/glycosphingolipids biosynthesis, and fat metabolism, along with advanced clinicopathological features. It also exhibited low mutation rates and microsatellite instability, associating it with the mesenchymal phenotype. In contrast, the low-risk group showed higher tumor content and upregulated protein and sugar metabolism. We identified a 15-gene prognostic signature representing these characteristics, including CPVL, KYNU, CD36, and GPX3, strongly correlated with M2 macrophages, validated through single-cell analysis and an internal cohort. Despite resistance to immunotherapy, the high-risk group showed sensitivity to molecular targeted agents directed at IGF-1R (BMS-754807) and the PI3K-mTOR pathways (AZD8186, AZD8055). We experimentally validated these promising drugs for their inhibitory effects on MKN45 and MKN28 gastric cells. This study unveils the intricate interplay between TME and metabolic pathways in gastric cancer, offering potential for enhanced diagnosis, patient stratification, and personalized treatment. Understanding molecular features in each subtype enriches our comprehension of gastric cancer heterogeneity and potential therapeutic targets.

IMG-02. USING MRI RADIOMICS AND MACHINE LEARNING TO PREDICT OVERALL SURVIVAL FOR PEDIATRIC DIFFUSE MIDLINE GLIOMAS

Abstract BACKGROUND Pediatric diffuse midline gliomas (DMG) are aggressive central nervous system tumors with a median overall survival (OS) of less than one year from diagnosis. There is no curative therapy for DMG, and radiation therapy (RT) is the standard treatment. Magnetic resonance imaging (MRI) is the common noninvasive tool for DMG diagnosis and monitoring of tumor response to therapy. We developed an automatic pipeline to segment subregions of DMG and select MRI features that predict patient OS. METHODS We acquired diagnostic and post-RT multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations of pediatric DMG patients from two centers: 53 from Children’s National Hospital (internal) and 16 from Children’s Hospital of Philadelphia (external). We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned the model to segment tumor core (TC) and whole tumor (WT) of DMG. We used PyRadiomics and sequential feature selection to select discriminative features based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features. RESULTS For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Heterogeneous WT intensity in baseline T2 FLAIR and smaller post-RT TC/WT volume ratio indicate longer OS. CONCLUSIONS Our method accurately predict OS for DMG patients and can be extended to other rare pediatric brain tumors. With automated and standardized analysis, early prognostication of OS can guide patient risk stratification and clinical decisions.

A machine learning-based approach for predicting renal function recovery in general ward patients with acute kidney injury.

Acute kidney injury (AKI) is a significant challenge in healthcare. While there are considerable researches dedicated to AKI patients, a crucial factor in their renal function recovery, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning model to predict restoration of kidney function in patients with AKI. Our study encompassed data from 350,345 cases, derived from three hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features for analysis. Our analysis contained 7,041 and 2,929 patients' data from internal cohort and external cohort respectively. The Categorical Boosting Model demonstrated significant predictive accuracy, as evidenced by an internal area under the receiver operating characteristic (AUROC) of 0.7860, and an external AUROC score of 0.7316, thereby confirming its robustness in predictive performance. SHapley Additive exPlanations (SHAP) values were employed to explain key factors impacting recovery of renal function in AKI patients. This study presented a machine learning approach for predicting renal function recovery in patients with AKI. The model performance was assessed across distinct hospital settings, which revealed its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real- world.

CT-Based AI model for predicting therapeutic outcomes in ureteral stones after single extracorporeal shock wave lithotripsy through a cohort study.

Exploring the efficacy of an artificial intelligence (AI) model derived from the analysis of CT images to precisely forecast the therapeutic outcomes of singular-session extracorporeal shock wave lithotripsy (ESWL) in the management of ureteral stones. A total of 317 patients diagnosed clinically with ureteral stones were included in this investigation. Unenhanced CT was administered to the participants within the initial fortnight preceding the inaugural ESWL. The internal cohort consisted of 250 individuals from a local healthcare facility, whereas the external cohort comprised 67 participants from another local medical institution. The proposed framework comprises three main components: an automated semantic segmentation model developed using 3D U-Net, a feature extractor that integrates radiomics and autoencoder techniques, and an ESWL efficacy prediction model trained with various machine learning algorithms. All participants underwent thorough postoperative follow-up examinations four weeks hence. The efficacy of ESWL was defined by the absence of stones or residual fragments measuring ≤2mm in KUB X-ray assessments. Model stability and generalizability were judiciously validated through a fivefold cross-validation approach and a multi-center external test strategy. Moreover, Shapley Additive Explanations (SHAP) values for individual features were computed to elucidate the nuanced contributions of each feature to the model's decision-making process. The semantic segmentation model we constructed exhibited an average Dice coefficient of 0.88 ± 0.08 on the external testing set. ESWL classifiers built using Support Vector Machine (SVM), Random Forest (RF), XGBoost (XB), and CatBoost (CB) achieved AUROC values of 0.78, 0.84, 0.85, and 0.90, respectively, on the internal validation set. For the external testing set, SVM, RF, XB, and CB predicted ESWL with AUROC values of 0.68, 0.79, 0.80, and 0.83, respectively, with the last one being the optimal algorithm. The radiomics features and auto-encoder features made significant contributions to the decision-making process of the classification model. This investigation unmistakably underscores the remarkable predictive prowess exhibited by a scrupulously crafted AI model using CT images to precisely anticipate the therapeutic results of a singular session of extracorporeal shock wave lithotripsy for ureteral stones.

Serum metabolomics analysis of patients with chronic obstructive pulmonary disease and 'frequent exacerbator' phenotype.

Chronic obstructive pulmonary disease (COPD) exacerbations accelerate loss of lung function and increased mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations. The present study aimed to assess the metabolic characteristics of the frequent exacerbation of COPD (COPD‑FE) phenotype, identify potential metabolic biomarkers associated with COPD‑FE risk and evaluate the underlying pathogenic mechanisms. An internal cohort of 30 stable patients with COPD was recruited. A widely targeted metabolomics approach was used to detect and compare serum metabolite expression profiles between patients with COPD‑FE and patients with non‑frequent exacerbation of COPD (COPD‑NE). Bioinformatics analysis was used for pathway enrichment analysis of the identified metabolites. Spearman's correlation analysis assessed the associations between metabolites and clinical indicators, while receiver operating characteristic (ROC) analysis evaluated the ability of metabolites to distinguish between two groups. An external cohort of 20 patients with COPD validated findings from the internal cohort. Out of the 484 detected metabolites, 25 exhibited significant differences between COPD‑FE and COPD‑NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman's correlation analysis demonstrated associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis demonstrated that the area under the curve (AUC) values for D‑fructose 1,6‑bisphosphate (AUC=0.871), arginine (AUC=0.836), L‑2‑hydroxyglutarate (L‑2HG; AUC=0.849), diacylglycerol (DG) (16:0/20:5) (AUC=0.827), DG (16:0/20:4) (AUC=0.818) and carnitine‑C18:2 (AUC=0.804) were >0.8, highlighting their discriminative capacity between the two groups. External validation results demonstrated that DG (16:0/20:5), DG (16:0/20:4), carnitine‑C18:2 and L‑2HG were significantly different between patients with COPD‑FE and those with COPD‑NE. In conclusion, the present study offers insights into early identification, mechanistic understanding and personalized management of the COPD‑FE phenotype.

Predicting COPD exacerbations based on quantitative CT analysis: an external validation study.

Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations. Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification. A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82-0.92) in the internal cohort and 0.86 (95%CI, 0.81-0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events. The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.

Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

Prediction of ovarian cancer prognosis using statistical radiomic features of ultrasound images

Objective. Ovarian cancer is the deadliest gynecologic malignancy worldwide. Ultrasound is the most useful non-invasive test for preoperative diagnosis of ovarian cancer. In this study, by leveraging multiple ultrasound images from the same patient to generate personalized, informative statistical radiomic features, we aimed to develop improved ultrasound image-based prognostic models for ovarian cancer. Approach. A total of 2057 ultrasound images from 514 ovarian cancer patients, including 355 patients with epithelial ovarian cancer, from two hospitals in China were collected for this study. The models were constructed using our recently developed Frequency Appearance in Multiple Univariate pre-Screening feature selection algorithm and Cox proportional hazards model. Main results. The models showed high predictive performance for overall survival (OS) and recurrence-free survival (RFS) in both epithelial and nonepithelial ovarian cancer, with concordance indices ranging from 0.773 to 0.794. Radiomic scores predicted 2 year OS and RFS risk groups with significant survival differences (log-rank test, P < 1.0 × 10−4 for both validation cohorts). OS and RFS hazard ratios between low- and high-risk groups were 15.994 and 30.692 (internal cohort) and 19.339 and 19.760 (external cohort), respectively. The improved performance of these newly developed prognostic models was mainly attributed to the use of multiple preoperative ultrasound images from the same patient to generate statistical radiomic features, rather than simply using the largest tumor region of interest among them. The models also revealed that the roundness of tumor lesion shape was positively correlated with prognosis for ovarian cancer. Significance. The newly developed prognostic models based on statistical radiomic features from ultrasound images were highly predictive of the risk of cancer-related death and possible recurrence not only for patients with epithelial ovarian cancer but also for those with nonepithelial ovarian cancer. They thereby provide reliable, non-invasive markers for individualized prognosis evaluation and clinical decision-making for patients with ovarian cancer.

An MRI-based multi-parameter clinical decision support system (NeoMDSS) for early prediction of pathological complete response after the first cycle of neoadjuvant therapy in breast cancer: A multi-center prospective observational cohort study.

592 Background: Currently, there is no reliable and convenient way to predict response rates in early stages of neoadjuvant therapy (NAT) for breast cancer, which can affect the prognosis of non-responsive patients. Methods: In this prospective multi-center observational study, eligible participants who underwent NAT followed by radical surgery were recruited. All patients underwent magnetic resonance imaging (MRI) examination before and after the first cycle of NAT (1st-NAT). We analyzed the effects of clinicopathological features, such as histological grade, hormone receptor (HR) and HER2 status, Ki-67 expression, blood cell analysis, and MRI parameters including the reduction ratio of tumor diameter (ΔD%), apparent diffusion coefficient (ADC), and early enhancement ratio (EER) on NAT. By incorporating independent influencing factors, we developed a multiparametric clinical decision support system (NeoMDSS) model based on a retrospective cohort and validated its accuracy in both a prospective single-center internal cohort and a prospective multi-center external cohort. Relevant clinical trial details can be found under the identifier NCT04909554. Results: A total of 301 breast cancer patients were enrolled between January 2019 and December 2023, including 140 patients in the training cohort, 120 patients in the internal validation cohort, and 41 patients in the external validation cohort. The NeoMDSS model demonstrated excellent performance in predicting pathological complete response (pCR) at 1st-NAT, with an AUC of 0.874 (95% CI 0.813-0.935) in the training cohort, 0.845 (95% CI 0.771-0.919) in the internal validation cohort, and 0.867 (95% CI 0.742-0.992) in the external validation cohort. The NeoMDSS model exhibited a sensitivity, specificity, and accuracy of 80.0%, 81.3%, and 80.5%, respectively, in the internal validation cohort, and 87.0%, 83.3%, and 85.4%, respectively, in the external validation cohort. Calibration curves and decision curve analysis (DCA) further supported the clinical value of the NeoMDSS model. To facilitate the clinical application of the NeoMDSS model, a nomogram and an online website ( www.gdphneomdss.com ) were developed to calculate the probability of pCR, which the sensitivity, specificity, and accuracy of the NeoMDSS model were all 80.0% in the internal validation cohort and 80.0%, 81.3%, and 80.5% in the external validation cohort. Conclusions: The NeoMDSS model serves as a convenient tool in clinical practice at 1st-NAT to calculate and accurately predict the probability of pCR after NAT. This assists clinicians in determining whether to modify the treatment plan and improving personalized treatment for patients with poor response to NAT. Clinical trial information: NCT04909554 .

The combined use of serum Raman spectroscopy and D dimer testing for the early diagnosis of acute aortic dissection

ObjectivesAcute aortic dissection (AAD) is an extremely life-threatening medical emergency, often misdiagnosed in its early stages, resulting in prolonged wait times for rescue. This study aims to identify potential serum biomarkers that can assist in the accurate diagnosis of AAD and effectively differentiate it from other conditions causing severe chest pain. MethodsA total of 122 patients with AAD and 129 patients with other severe chest pain disorders were included in the study. Serum samples were analyzed by measuring the peak intensities of Raman spectra. For each measurement, the Raman spectrum was accumulated by two accumulations (3 s per acquisition). Logistic regression and nomogram models were developed using these peak intensities as well as D-dimer levels to predict the occurrence of AAD. The clinical utilities of these models were assessed through receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) in both training and internal test cohorts. ResultsThe D-dimer levels of AAD patients were significantly increased, as well as higher intensities at specific Raman peaks, including 505 cm−1, 842 cm−1, 947 cm−1, 1254 cm−1, 1448 cm−1, and 1655 cm−1 when compared to non-AAD patients. Conversely, decreased intensities were observed at Raman peaks such as 750 cm−1, 1004 cm−1, 1153 cm−1, 1208 cm−1, and 1514 cm−1 in AAD patients. Least absolute shrinkage and selection operator regression analysis on the training cohort identified eight potential predictors: D-dimer along with intensity measurements at peaks such as 505 cm−1, 750 cm−1, 1153 cm−1, 1208 cm−1, 1254 cm−1, 1448 cm−1, and 1655 cm−1. The combination of these eight potential predictors demonstrated a good discriminatory performance, with an area under the curve (AUC) value of 0.928 in the training cohort and an AUC of 0.936 in the internal test cohort, outperforming the use of D-dimer alone. Furthermore, DCA curve analysis revealed that leveraging this combination of eight potential predictors would provide substantial net benefits for clinical application. Moreover, this combination significantly augmented discrimination power, as evidenced by a continuous NRI of 39.8 % and IDI of 9.95 % in the training cohort, as well as a continuous NRI of 27.1 % and IDI of 9.95 % in the internal test cohort. ConclusionsThe employment of this combination of eight potential predictors effectively rules out AAD to a greater extent. This study presents a promising diagnostic strategy for early detection using optical diagnostic techniques such as Raman spectroscopy.