Training Cohort Research Articles

Abstract B075: Detection of early gastric carcinoma by micronuclei DNA from erythrocytes

Abstract Background: Advancements in non-invasive diagnostic technologies for gastric carcinoma (GC), including serum microRNA and cell-free DNA assays, have shown promise but still face challenges in achieving consistent early detection. Micronuclei (MN), extranuclear bodies harboring fragmented chromosomal segments, are indicative of genomic instability and have been employed in genotoxicity assessments and cancer diagnostics. We have developed a novel technique (WO2021/228246 A1) for the purification and characterization of micronuclei DNA (MN-DNA) extracted from erythrocytes in peripheral blood. Here, we evaluated the potential of micronuclei DNA (MN-DNA) from 2ml peripheral blood for early gastric carcinoma detection and explored its application for early cancer diagnosis. Methods: We assembled a clinical cohort consisting of 425 healthy donors (HDs) and 282 gastric cancer patients, from whom 1-2 mL peripheral blood samples were collected. Participants were randomly allocated into training, validation, and independent test cohorts in a 7:2:1 ratio, ensuring similar distributions of cancer stages and gender. MN-DNA was isolated from erythrocytes and subjected to comprehensive whole-genome sequencing. Predictive machine learning models were constructed utilizing distinct tumor-associated MN-DNA features identified through genome-wide analysis to distinguish between HDs and GC. Results: Comprehensive genome-wide analysis of MN-DNA demonstrated pronounced disparities in distribution patterns between HDs and GC patients. The predictive model built on MN-DNA features achieved an AUC of 97.69% (95%CI: 95.57- 99.81%), with an 96.47% specificity and 91.07% sensitivity in the independent test cohort. The model identified early GC (stage 0-I) and advanced GC (stage II-IV) with sensitivities of 95.24% or 78.57%, respectively. Conclusions: Our results demonstrate that MN-DNA from 1-2mL peripheral blood enable accurate detection of GC, especially in early GC. As a type of cytoplasmic DNA, MN-DNA can provide a valuable tool for early cancer detection, offering different underlying mechanisms compared to current methods. Research sponsor: Timing Biotech. Citation Format: Yuehua Han, Xingyun Yao, Haobo Sun, Cheng Fang, Peiwei Li, Honghao Liang, Jie Jin, Xiaofei Gao. Detection of early gastric carcinoma by micronuclei DNA from erythrocytes [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B075.

Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images.

Automatic primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) is a quite challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution. Therefore, most recently proposed methods based on radiomics or deep learning (DL) is difficult to achieve good results on CT datasets. A peritumoral radiomics-guided generative adversarial network (PRG-GAN) was proposed to address this challenge. A total of 157 NPC patients with CT images was collected and divided into training, validation, and testing cohorts of 108, 9, and 30 patients, respectively. The proposed model was based on a standard GAN consisting of a generator network and a discriminator network. Morphological dilation on the initial segmentation results from GAN was first conducted to delineate annular peritumoral region, in which radiomics features were extracted as priori guide knowledge. Then, radiomics features were fused with semantic features by the discriminator's fully connected layer to achieve the voxel-level classification and segmentation. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were used to evaluate the segmentation performance using a paired samples t-test with Bonferroni correction and Cohen's d (d) effect sizes. A two-sided p-value of less than 0.05 was considered statistically significant. The model-generated predictions had a high overlap ratio with the ground truth. The average DSC, HD95, and ASSD were significantly improved from 0.80±0.12, 4.65±4.71mm, and 1.35±1.15mm of GAN to 0.85±0.18 (p=0.001, d=0.71), 4.15±7.56mm (p=0.002, d=0.67), and 1.11±1.65mm (p<0.001, d=0.46) of PRG-GAN, respectively. Integrating radiomics features into GAN is promising to solve unclear border limitations and increase the delineation accuracy of GTVp for patients with NPC.

Abstract A041: Detection of early-stage gastrointestinal cancers using micronuclei DNA from erythrocytes

Abstract Background: Gastrointestinal (GI) cancers account for over a third of cancer deaths. However, their early detection remains a challenge. Micronuclei (MN) are cytoplasm-resident subcellular structures, containing damaged chromosome segments that indicative of genomic instability. Increased MN frequency in hematopoietic cells is observed in patients with solid tumors. Our developed technique (WO2021/228246 A1) enables the purification and characterization of micronuclei DNA in erythrocytes from peripheral blood. By comparing MN-DNA from healthy donors (HDs) and GI cancer patients, we identified significant changes in read densities at specific genomic locations in patients, which were termed as tumor-associated MN-DNA (taMN- DNA) features. Here, we evaluated the potential of these features for early-stage GI cancer detection, including colorectal (CC), gastric (GC) and esophageal (EC) cancers. Methods: Peripheral blood (1-2 mL) was collected from healthy donors (HD) and cancer patients MN-DNA isolation and purification from erythrocytes. Participants were randomly divided into training, validation and independent test cohorts in a 7:2:1 ratio, maintaining similar cancer types, gender and age distribution. Distinct MN-DNA features between cancer patients and HDs were identified using sequencing data. Machine learning algorithms were applied using these features for cancer detection. Results: The study enrolled 1753 participants, including 987 HDs, 420 CC, 282 GC and 64 EC cases. Of these, over half were diagnosed with early-stage diseases; TNM stage 0-I accounted for 40.57%, stage II for 23.32%, stage III for 29.25% and stage IV for 6.87%. The pan-cancer model achieved 88.2% sensitivity with an overall specificity of 95.4%. Patients in individual caner types were detected from the tissue of origin classification with an overall accuracy of 91.9% based on pan-cancer at 95.4% specificity. Sensitivity was 90.8% for CC, 91.7% for GC and 100% for EC. For early- stage (stage 0-II) CC, GC and EC, the model demonstrated sensitivities of 97.4%, 94.7% and 100.00%, respectively, at 95.4% specificity. Conclusions: Unlike cell-free DNA, MN-DNA are chromosomal fragments in the cytoplasm. The abundance of erythrocytes in peripheral blood provides an easily accessible source for MN-DNA enrichment. This pilot study illustrates the potential of MN-DNA as a tool for early GI cancer detection, contributing to large-scale efforts towards developing an effective GI cancer screening test. Research sponsor: Timing Biotech. Citation Format: Haobo Sun, Xingyun Yao, Yuehua Han, Yurong Jiao, Xiangxing Kong, Chengcheng Liu, Qingqu Guo, Fei Meng, Honghao Liang, Hongbo Li, Xiuqin Fan, Jun Li, Xiaofei Gao. Detection of early-stage gastrointestinal cancers using micronuclei DNA from erythrocytes [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A041.

Radiomics analysis of dual-energy CT-derived iodine maps for differentiating malignant from benign thyroid nodules.

Many thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual-energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking. To determine the ability of an iodine map-based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules. A total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad-score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed. According to the clinical model, age (0.989 [0.984, 0.995]; p<0.001), NIC (0.778 [0.640, 0.995]; p=0.01), and cyst changes (0.617 [0.507, 0.751]; p<0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p=0.01), NIC (0.797 [0.674, 0.941]; p=0.008), cyst changes (0.786 [0.653, 0.947]; p=0.01), and the rad-score (1.106 [1.070, 1.143]; p<0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96vs. 0.87; p=0.01) and test (AUC, 0.90vs. 0.79; p=0.04) cohorts than did the clinical model. The radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules.

Abstract 4135025: Inflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease

Background: Coronary angiography, an invasive method, remains the gold standard technique for coronary artery disease (CAD) diagnosis in clinical practice. However, due to its invasive nature, it cannot be used for mass population screening. There is an urgent need in the clinical to identify new liquid biopsy molecular markers for screening patients with CAD. Methods: We utilized 5hmC-Seal to generate genome-wide 5hmC profiles in plasma cell-free DNA (cfDNA) from 724 CAD patients. To investigate the correlation between 5hmC modifications and CAD, we separated patients into training and validation cohorts and developed a 5hmC-based logistic regression model from the training cohort to predict CAD patients in the validation cohort. Furthermore, we incorporated an external cohort comprising 167 samples to validate the reliability of our model. Results: We found that the differentially hydroxymethylated genes (DhMGs) in people with the normal coronary artery (NCA), nonfatal stable CAD (sCAD), non-ST-elevation myocardial infarction (NSTEMI), as well as ST-elevation myocardial infarction (STEMI) enriched in neutrophil-mediated inflammatory response pathways, and inflammation-related DhMGs can reflect the progression of atherosclerotic plaque. Furthermore, we used machine learning algorithms to develop a predictive model for predicting the occurrence of myocardial infarction (MI) based on 19 inflammatory markers, and the predictive model could effectively predict MI (NSTEMI and STEMI) (AUC = 0.913). Simultaneously, our model has demonstrated strong performance in external validation. Conclusions: We have identified a novel biomarker, the inflammation-associated 5hmC markers, which can reflect the clinical manifestations of CAD and can effectively predict the occurrence of MI.

A 18F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.

To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC). The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature. The DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively. Through the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.

Development and validation of a nomogram for predicting the risk of developing gastric cancer based on a questionnaire: a cross–sectional study

BackgroundDetection of gastric cancer (GC) at early stages is an effective strategy for decreasing mortality. This study aimed to construct a prediction nomogram based on a questionnaire to assess the risk of developing GC.MethodsOur study comprised a total of 4379 participants (2326 participants from outpatient at Fengqing People’s Hospital were considered for model development and internal validation, and 2053 participants from outpatients at the endoscopy center at the Seventh Affiliated Hospital of Sun Yat-Sen University were considered for independent external validation) and gastric mucosa status was determined by endoscopy and biopsies. The eligible participants in development cohort from Fengqing people’s Hospital were randomly separated into a training cohort (n=1629, 70.0%) and an internal validation cohort (n=697, 30.0%). The relevant features were selected by a least absolute shrinkage and selection operator (LASSO), and the ensuing features were evaluated through multivariable logistic regression analysis. Subsequently, the variables were selected to construct a prediction nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated by the C-index and calibration plot, respectively. Decision curve analysis (DCA) curves were used for the assessment of clinical benefit of the model. This model was developed to estimate the risk of developing neoplastic lesions according to the “transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” (TRIPOD) statement.ResultsSix variables, including age, sex, alcohol consumption, cigarette smoking, education level, and Hp infection status, were independent risk factors for the development of neoplastic lesions. Thus, these variables were incorporated into the final nomogram. The AUC of the nomogram were 0.701, 0.657 and 0.699 in the training, internal validation, and external validation cohorts, respectively. The calibration curve showed that the nomogram was in good agreement with the observed outcomes. Compared to treatment of all patients or none, our nomogram showed a notably higher clinical benefit.ConclusionThis nomogram proved to be a convenient, cost-effective tool to effectively predict an individual’s risk of developing neoplastic lesions, and it can act as a prescreening tool before gastroscopy.

Prognosis analysis and nomogram for predicting lateral lymph node metastasis in Medullary Thyroid Microcarcinoma.

Currently, the incidence rate of Medullary Thyroid Microcarcinoma (micro-MTC) has an increasing trend, but the incidence of LNM and prognosis were still ambiguous. We analyzed the status of neck LNM of micro-MTC patients and created a prognostic nomogram to predict the probability of lateral lymph node metastasis (LLNM) for clinical practice. This is a retrospective study included patients with micro-MTC from SEER database for the period from 2004 to 2017 and patients from our medical center for the period from 2011 to 2019. A nomogram was constructed and the accuracy and clinical practicability were separately tested by Harrell's C-indexes, calibration plots, Receiver operating characteristic curve (ROC) and decision curve analyses (DCA). A total of 413 patients with micro-MTC from SEER database and 64 patients with micro-MTC from our department enrolled in the study. There were 16.0% and 9.4% cases in SEER database and 39.1% and 25.0% cases in our department appeared LNM and LLNM, respectively. Besides, a nomogram was constructed to assess the incidence of LLNM with good C-index, which was 0.850 in training cohort and 0.856 in validation cohort. The results of the area under the curve (AUC) were 0.830 in training cohort, 0.801 in validation cohort and 0.832 in external testing cohort, respectively. A relatively high rate of LLNM than expected was found, which should be emphasized. The prediction model could facilitate clinicians to assess the probability of LLNM and make a personalized treatment strategy.

Risk factors and predictive model development for high blood loss in minimally invasive distal pancreatectomy: a retrospective cohort study.

High blood loss is an adverse event related to increased morbidity and poorer outcomes in pancreatic surgery patients. The aim of this study was to identify risk factors and establish a predictive model for high perioperative blood loss (HPBL) in minimally invasive distal pancreatectomy (MIDP). We collected data from 353 patients who underwent MIDP at a university affiliated tertiary hospital between January 2016 and October 2023. Perioperative blood loss was calculated based on pre- and postoperative hemoglobin concentrations according to a combination of the formulas provided by Nadler and Gross. Multivariate logistic regression analyses were performed for the training cohort to identify the clinical factors independently associated with perioperative blood loss (PBL). A predictive nomogram based on these factors was established and validated. Weight, imaging findings, serum albumin concentration, MIDP experience, spleen treatment, and operation time were independent predictors for HPBL. The established model for predicting HPBL showed that the area under the curve (AUC) was 0.799 (95% CI = 0.746-0.853) and 0.852 (95% CI = 0.760-0.943) for the training cohort and validation cohort, respectively. When utilized to predict blood transfusion, the AUC was 0.778 (95% CI = 0.691-0.865) in the training cohort and 0.818 (95% CI = 0.681-0.955) in the validation cohort. Patients with a high predicted risk had significantly higher incidences of postoperative pancreatic fistula, intra-abdominal infection, and longer hospital stays than patients with a low risk. We established and validated a model for predicting HPBL in MIDP patients. This novel model may have future utility when generating surgical strategies.

ViT-based quantification of intratumoral heterogeneity for predicting the early recurrence in HCC following multiple ablation.

This study aimed to develop a quantitative intratumoral heterogeneity (ITH) model for assessing the risk of early recurrence (ER) in pre-treatment multimodal imaging for hepatocellular carcinoma (HCC) patients undergoing ablation treatments. This multi-centre study enrolled 633 HCC patients who underwent ultrasound-guided local ablation between January 2015 and September 2022. Among them, 422, 85, 57 and 69 patients underwent radiofrequency ablation (RFA), microwave ablation (MWA), laser ablation (LA) and irreversible electroporation (IRE) ablation, respectively. Vision-Transformer-based quantitative ITH (ViT-Q-ITH) features were extracted from the US and MRI sequences. Multivariable logistic regression analysis was used to identify variables associated with ER. A combined model integrated clinic-radiologic and ViT-Q-ITH scores. The prediction performance was evaluated concerning calibration, clinical usefulness and discrimination. The final training cohort and internal validation cohort included 318 patients and 83 patients, respectively, who underwent RFA and MWA. The three external testing cohorts comprised of 106 patients treated with RFA, 57 patients treated with LA and 69 patients who underwent IRE ablation. The combined model showed excellent predictive performance for ER in the training (AUC: .99, 95% CI: .99-1.00), internal validation (AUC: .86, 95% CI: .78-.94), external testing (AUC: .83, 95% CI: .73-.92), LA (AUC: .84, 95% CI: .73-.95) and IRE (AUC: .82, 95% CI: .72-.93) cohorts, respectively. Decision curve analysis further affirmed the clinical utility of the combined model. The multimodal-based model, incorporating clinic-radiologic factors and ITH features, demonstrated superior performance in predicting ER among early-stage HCC patients undergoing different ablation modalities.

SURG-21. DEVELOPMENT AND VALIDATION OF A CLINICAL RISK SCORE FOR POSTOPERATIVE OUTCOME IN NEWLY DIAGNOSED GLIOBLASTOMA: A REPORT OF THE RANORESECT GROUP

Abstract BACKGROUND Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aim to make use of our RANO classification for extent of resection (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. METHODS The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. RESULTS Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy (TMZ/RT→TMZ). Residual tumor, MGMT promotor methylation status, age, and KPS were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p&amp;lt;0.01). The presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma (n = 258, p&amp;lt;0.01). Compared to previously postulated models, goodness-of-fit measurements were superior for our score. CONCLUSIONS The novel RANO risk score serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The score may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Screening high-risk individuals for primary gastric adenocarcinoma: evaluating progression-free survival probability score in the presence and absence of Rictor expression after gastrectomy

ObjectiveDeveloping nomogram-based risk stratification model to determine 3-year and 5-year progression-free survival (PFS) and to identify high-risk patients with gastric adenocarcinoma based on different Rictor statuses.Methods1366 individuals who underwent radical gastric surgery to treat gastric adenocarcinoma at Shanxi Cancer Hospital from May 2002 to December 2020 were analyzed. Cox regression analysis was employed to create the nomograms. The nomograms’ performance was assessed using C-index, time receiver operating characteristic (t-ROC) curves, calibration curves, and decision curve analysis (DCA) curves in training and validation cohorts. Subsequently, patients were categorized into high-risk and low-risk groups based on the nomogram’s risk scores.ResultsThe Rictor (-) nomogram for predicting PFS included variables such as age, number of positive lymph nodes, vascular invasion, maximum diameter of the tumor, omentum metastasis, and expression of MSH2. In the internal validation, the C-index of the Rictor (-) nomogram was 0.760 (95%CI: 0.720-0.799), which was superior to the C-index of the American Joint Committee on Cancer (AJCC) 8th edition TNM staging (0.683, 95%CI: 0.646-0.721). Similarly, the Rictor (+) nomogram for predicting PFS included variables such as gender, age, pT stage, number of positive lymph nodes, neural invasion, maximum diameter of the tumor, omentum metastasis, Clavien-Dindo classification for complications, and CGA expression. The C-index of the Rictor (+) nomogram was 0.795 (95%CI: 0.764-0.825), which outperformed the C-index of the AJCC 8th edition TNM staging (0.693, 95%CI: 0.662-0.723). The calibration curves, t-ROC curves, and decision curve analysis for both nomogram models demonstrated their excellent prediction ability.ConclusionThis study presents the first risk stratification for Rictor status in gastric adenocarcinoma. Our model identifies low-risk patients who may not require additional postoperative treatment, while high-risk patients should consider targeted therapies that specifically target Rictor-positive indicators.

Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches.

To compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) and further evaluate the stratified effect of the radiomics model on disease-free survival (DFS) and overall survival (OS) of LSCC patients. In all, 106 LSCC patients (40 with inflamed and 66 with non-inflamed immunophenotyping) were randomly assigned into a training (n = 53) and testing (n = 53) cohort. Briefly, 750 radiomics features from contrast-enhanced CT images were respectively extracted from the whole tumor and two Otsu method-derived subregions. Intraclass correlation coefficients (ICCs) were calculated to evaluate the reproducibility. The radiomics models for predicting immunophenotyping were respectively created using K-nearest neighbors (KNN), logistic regression (LR), and Naive bayes (NB) classifiers. The performance of models in the testing cohort were compared using area under the curve (AUC). The prognostic value of the optimal model was determined by survival analysis. The radiomics features derived from whole tumor showed better reproducibility than those derived from habitats. The best model for the whole tumor (LR classifier) showed superior performance than that for the habitats (KNN classifier) in the testing cohort, but there were no significant differences (AUC: 0.741 vs. 0.611, p = 0.112). Multivariable Cox regression analysis showed that the immunophenotyping predicted by the optimal model was an independent risk factor of unfavorable DFS (p = 0.009) and OS (p = 0.008) in LSCC patients. Whole tumor-based CT radiomics could serve as a potential predictive biomarker of immunophenotyping and outcome prediction in LSCC patients.

The Risk Assessment Before Dose Tapering Among Methadone Maintenance Treatment Participants: Derivation and Validation of a Nomogram

ABSTRACT Many methadone maintenance treatment (MMT) participants experienced a tapering phase. The benefit of tapering is based on a balance between meeting the desire to reduce methadone dose and reduction in relapse. We aimed to develop and validate a nomogram to assess relapse risk after dose tapering. We developed and internally validated a nomogram for risk assessment before dose tapering in 432 participants with dose tapering in the non-Guangzhou region of Guangdong, China, and externally validated it in 117 participants with dose tapering in Guangzhou. Cox regression analysis showed that the taper start week (HR = 0.14, [0.08–0.22]) was an independent risk predictor of the relapse risk after tapering. The C-index of the nomogram was 0.76 (95%CI: 0.73–0.79) in the training cohort, 0.76 (95%CI: 0.72–0.80) in the testing cohort, and 0.84 (95%CI: 0.80–0.88) in the validation cohort. Decision curve analysis showed that the nomogram had better discriminative ability than other predictors. The nomogram was developed to assess the risk of relapse for MMT participants who volunteer a tapering phase and may help participants better make decisions about whether and how to reduce the dose to minimize the harm of relapse.

Building a machine learning-based risk prediction model for second-trimester miscarriage

BackgroundSecond-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of research on predictive models for the risk of second-trimester miscarriage.MethodsClinical data were retrospectively collected from patients who were in the second trimester of pregnancy (between 14+0 and 27+6 weeks gestation), whose main diagnosis was “threatened abortion” and who were hospitalized at the Women and Children’s Hospital of Ningbo University from January 2020 to October 2023. Following preliminary data processing, the patient cohort was randomly stratified into a training cohort and a validation cohort at proportions of 70% and 30%, respectively. The Boruta algorithm and multifactor analysis were used to refine feature factors and determine the optimal features linked to second-trimester miscarriages. The imbalanced dataset from the training cohort was rectified by applying the SMOTE oversampling approach. Seven machine-learning models were built and subjected to a comprehensive analysis to validate and evaluate their predictive capabilities. Through this rigorous assessment, the optimal model was selected. Shapley additive explanations (SHAP) were generated to provide insights into the model’s predictions, and a visual representation of the predictive model was built.ResultsA total of 2006 patients were included in the study; 395 (19.69%) of them had second-trimester miscarriages. XGBoost was shown to be the optimal model after a comparison of seven different models utilizing metrics such as accuracy, precision, recall, the F1 score, precision-recall average precision, the receiver operating characteristic-area under the curve, decision curve analysis, and the calibration curve. The most significant feature was cervical length, and the top ten features of second-trimester miscarriage were found using the SHAP technique based on relevance rankings.ConclusionThe risk of a second-trimester miscarriage can be accurately predicted by the visual risk prediction model, which is based on the machine learning mentioned above.

Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection

PurposeTo explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection.MethodsTotal 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. The best-performing model was compared with the widely used staging systems and preoperative prognostic indexes.ResultsThe RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes.ConclusionBoth radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.

Development and validation of a nomogram for predicting postpartum hemorrhage in women with preeclampsia: A retrospective case-control study.

This retrospective case-control study aimed to develop a nomogram for predicting postpartum hemorrhage in women with preeclampsia. This study was carried out at the Fujian Maternity and Child Health Hospital, involving 542 preeclampsia patients who underwent vaginal deliveries. The participants were split into 2 groups: a training cohort (85%, n = 460) and a validation cohort (15%, n = 82). Least absolute shrinkage and selection operator regression was applied to pinpoint relevant risk factors by selecting appropriate candidate variables. Subsequently, multivariate logistic regression analysis was conducted on the training set, leading to the creation of a nomogram as a visual risk prediction tool. The model's performance was tested and verified internally and externally by examining receiver operating characteristic curves and calibration curves. The correlation heatmap revealed collinearity among variables, necessitating the use of least absolute shrinkage and selection operator regression to select 4 candidate variables. Multivariate logistic regression analysis identified significant associations with the following outcomes: white blood cell count (odds ratio [OR]: 2.485, 95% confidence interval [CI]: 1.483-4.166), third stage of labor (OR: 1.382, 95% CI: 1.182-1.616), anemia (OR: 9.588, 95% CI: 4.022-22.854), and labor analgesia (OR: 0.187, 95% CI: 0.073-0.477). These variables were utilized to construct the nomogram. The receiver operating characteristic curves demonstrated good predictive performance (area under the curve train = 0.867, area under the curve test = 0.882), and the calibration curve yielded a C-index of 0.867. The nomogram created in this study has good sensitivity and specificity to assess risk and support clinical decision-making for postpartum hemorrhage in women with preeclampsia.

Combined fibrinogen concentration and neutrophil-to-lymphocyte ratio, an integrative model of the inflammatory response and coagulation cascades, for predicting prognosis in patients with upper tract urothelial carcinoma.

Inflammation and coagulation cascades are closely correlated with cancer occurrence and progression. This study investigated the prognostic value of the combination of plasma fibrinogen level and neutrophil-to-lymphocyte ratio (F-NLR) in patients with upper tract urothelial carcinoma (UTUC). The predictive ability of the F-NLR for overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) was initially established and then further validated in patients who underwent radical nephroureterectomy for UTUC. As a result, patients were divided into three groups following the establishment of cut-off values for the NLR (≥2.53 vs. <2.53) and fibrinogen (≥4.55 vs. <4.55) through receiver operating characteristic (ROC) curve analysis: F-NLR score 0 (low fibrinogen and low NLR), 2 (high fibrinogen and high NLR), or 1 (remaining patients). The F-NLR score was then identified as an independent risk factor for OS, CSS, and PFS (all P-value <0.05) by multivariate regression analysis in both the training and validation cohorts. In addition, F-NLR-based nomograms for OS, CSS, and PFS were developed and evaluated using the concordance index (C-index) and calibration curves. The integration of the F-NLR into existing nomograms improved predictive accuracy compared to the use of nomograms without the F-NLR score. This suggests that the addition of F-NLR is beneficial for enhancing the accuracy of prognosis prediction in patients with UTUC. The F-NLR score may serve as a powerful predictor for patients with UTUC.

Soluble and EV-bound CD27 act as antagonistic biomarkers in patients with solid tumors undergoing immunotherapy

BackgroundThe major breakthrough in cancer therapy with immune checkpoint inhibitors (ICIs) has highlighted the important role of immune checkpoints in antitumoral immunity. However, most patients do not achieve durable responses, making biomarker research in this setting essential. CD27 is a well known costimulatory molecule, however the impact of its soluble form in ICI is poorly investigated. Therefore, we aimed at testing circulating concentrations of soluble CD27 (sCD27) and CD27 bound to extracellular vesicles (EVs) as potential biomarkers to predict response and overall survival (OS) in patients undergoing ICI.MethodsSerum and plasma levels of sCD27 were assessed by immunoassay in three patient cohorts (n = 187) with advanced solid malignancies including longitudinal samples (n = 126): a training (n = 84, 210 specimens, Aachen ICI) and validation cohort (n = 70, 70 specimens, Hamburg ICI), both treated with ICI therapy, and a second independent validation cohort (n = 33, 33 specimens, Hamburg non-ICI) undergoing systemic therapy without any ICI. In a subset (n = 36, 36 baseline and 108 longitudinal specimens), EV-bound CD27 from serum was measured, while EV characterization studies were conducted on a fourth cohort (n = 45).ResultsIn the Aachen and Hamburg ICI cohorts, patients with lower circulating sCD27 levels before and during ICI therapy had a significantly longer progression-free survival (PFS) and OS compared to patients with higher levels, a finding that was confirmed by multivariate analysis (MVA) (Aachen ICI: pPFS = 0.012, pOS = 0.001; Hamburg ICI: pPFS = 0.040, pOS = 0.004) and after randomly splitting both cohorts into training and validation. This phenomenon was not observed in the Hamburg non-ICI cohort, providing a rationale for the predictive biomarker role of sCD27 in immune checkpoint blockade. Remarkably, EV-bound CD27 baseline levels and dynamics during ICI therapy also emerged as potent predictive biomarkers, acting however antagonistically to soluble sCD27, i.e. higher levels were associated with PFS and OS benefit. Combining both molecules (“multi-CD27” score) enhanced the predictive ability (HRPFS: 17.21 with p < 0.001, HROS: 6.47 with p = 0.011).ConclusionSoluble and EV-bound CD27 appear to have opposing immunomodulatory functions and may represent easily measurable, non-invasive prognostic markers to predict response and survival in patients undergoing ICI therapy.

The Demographic and Clinical Characteristics, Prognostic Factors, and Survival Outcomes of Head and Neck Carcinosarcoma: A SEER Database Analysis

Background: Head and neck carcinosarcoma (HNCS) is a rare and highly aggressive malignancy with limited research, resulting in an incomplete understanding of disease progression and a lack of reliable prognostic tools. This study aimed to retrospectively analyze the clinical characteristics and outcomes of HNCS patients using data from the Surveillance, Epidemiology, and End Results (SEER) database and to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS). Methods: Patients diagnosed with HNCS from 1975 to 2020 were identified in the SEER database. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic indicators, with the optimal model selected using the minimal Akaike Information Criterion (AIC). The identified prognostic factors were incorporated into nomograms to predict OS and CSS. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Survival curves were generated using Kaplan–Meier analysis and compared via the log-rank test. Results: A total of 152 HNCS patients were included, with 108 assigned to the training cohort and 44 to the validation cohort in a 7:3 ratio. Prognostic factors including age, primary tumor site, marital status, radiotherapy, chemotherapy, tumor size, pathological grade, and tumor stage were incorporated into the nomogram models. The models demonstrated strong predictive performance, with C-index values for OS and CSS of 0.757 and 0.779 in the training group, and 0.777 and 0.776 in the validation group, respectively. AUC values for predicting 3-, 5-, and 10-year OS were 0.662, 0.713, and 0.761, and for CSS the values were 0.726, 0.703, and 0.693. Kaplan–Meier analysis indicated significantly improved survival for patients with lower risk scores. The 3-, 5-, and 10-year OS rates for the entire cohort were 54.1%, 45.6%, and 35.1%, respectively, and the CSS rates were 62.9%, 57.5%, and 52.2%, respectively. Conclusions: This study provides validated nomograms for predicting OS and CSS in HNCS patients, offering a reliable tool to support clinical decision-making for this challenging malignancy. These nomograms enhance the ability to predict patient prognosis and personalize treatment strategies.