Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

The role of serun lipid, cytokine production in sudden sensorineural hearing loss

Sudden sensorineural hearing loss (SSNHL) has serious harm to human hearing health, where blood lipid and inflammatory levels may play a key role in it. Thus, the purpose of this investigation was to assess the connection between inflammatory and lipid variables and SSNHL. Patients diagnosed with SSNHL had an analysis of serum lipid parameters, such as total cholesterol (TC), triglycerides, HDL-C, LDL-C, apolipoprotein A (apo A), apolipoprotein B (apo B), and lipoprotein A (Lp(a)), as well as inflammatory factors like TNF-α and IL-10. After that, risk factor analysis was carried out utilizing univariate, multivariate regression, and LASSO retrospective modeling. In all, 72 SSNHL patients and 67 healthy control individuals were involved. The LDL/HDL, total cholesterol, ApoB, LP(a), IL-10, TNF-α, and IFN-γ considerably higher in the SSNHL group than in the healthy control group, however, nervonic acid and coenzyme Q were decreased significantly in SSNHL than Control group. The multivariate logistic regression model's analysis using multifactorial retrospective modeling revealed significant changes in LDL, LDL/HDL, IL-10, and TNF-α. In addition, in the LASSO regression model, the model demonstrated high discrimination, as evidenced by the C-index for the cohort's prediction nomogram, which was 0.998 (95% CI, 0.154-1.115) and confirmed to be 0.925 following bootstrapping validation. Finally, IL-10 and LDL/HDL were the main risk factors in SSNHL. LDL/HDL and IL-10 may be closely related to SSNHL's progress and should be evaluated promptly before treating patients with SSNHL.

Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography

Objectives

To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images.

Methods

A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve.

Results

The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model.

Conclusions

Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Prognostic power of radiomics in head and neck cancers: Insights from a meta-analysis

BACKGROUND

Prognostic modeling in head and neck cancers (HNC) has advanced with the integration of clinical factors and radiomic data from CT and MRI scans. However, previous reviews have not systematically evaluated the predictive performance of these models across different oncological endpoints or assessed factors affecting their generalizability. This study aims to fill this gap by providing a comprehensive analysis of prognostic models in HNC.

METHODS

Our systematic review and meta-analysis sourced data from PubMed, Embase, and Web of Science until August 30, 2023, shortlisting 16 studies. We concentrated on studies detailing HNC prognosis prediction through radiomics, which transparently tabulated performance metrics of c-index and utilized external validation sets. We excluded studies employing imaging techniques other than CT or MRI. Study quality was assessed using the QUIPS and RQS tools. Our meta-analysis comprised the radiomics prognosis model on all validation datasets, overall survival prediction with radiomics on all validation datasets, and overall survival prediction integrating clinical and radiomics data on external validation sets. All assessments adopted a random effects model. The research has been registered under CRD42023459049.

RESULTS

When evaluating by distinct endpoints, marked differences were observed. Delving deeper into the complexities of overall survival prediction, variables such as incorporation of clinical features and an enlarged training set were identified as major enhancers of the model's performance. Evaluating exclusively on external validation cohorts, purely clinical models demonstrated a prognostic strength of pooled 0.69 c-index for overall survival, in contrast to the 0.68 pooled c-index achieved by models rooted in radiomics. Combining both approaches elevated the pooled c-index to 0.76. It was clear that a blend of an expanded training dataset and features selected, coupled with the diversity in CT and MRI equipment and model counts, are pivotal in fortifying the model's resilience.

CONCLUSION

This systematic review and meta-analysis demonstrate that combining clinical and radiomic features significantly improves the predictive performance of prognostic models for overall survival in HNC. By systematically evaluating various endpoints and identifying key factors influencing model generalizability, our study fills a critical gap in the literature. These findings provide valuable insights for developing more accurate and personalized prognostic tools in HNC, guiding future research and enhancing clinical decision-making.

Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers

OBJECTIVE

Early and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic features extracted from ultrasound (US) images combined with machine learning (ML) methods in predicting ALNM to improve diagnostic accuracy and patient prognosis.

METHODS

In this retrospective study, data of 282 early-stage breast cancer patients from two centers were analyzed. We considered clinicopathological characteristics, conventional US features, contrast-enhanced ultrasound (CEUS) characteristics, and radiomics features. Radiomics features were extracted from US images, and using least absolute shrinkage and selection operator (LASSO) regression, 12 key features were selected to compute a Radiomics score (Rad-score). A nomogram was developed based on these features, alongside five ML models: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using metrics such as the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), negative predictive value (NPV), and positive predictive value (PPV).

RESULTS

Both the nomogram and ML models, including the Rad-score combined with histologic type, significantly predicted ALNM. Among all models, the XGBoost model showed the best performance with an AUC of 0.810 and an accuracy of 84.1% in the external test set, surpassing the nomogram and other ML models. SHapley Additive exPlanations (SHAP) analysis further provided insights into the influence of individual radiomics features on ALNM prediction.

CONCLUSIONS

While the nomogram provides a useful traditional statistical approach, integrating radiomics features with ML, particularly the XGBoost model enhanced by SHAP interpretability, offers superior predictive accuracy for ALNM in early-stage breast cancer patients.

Clinicopathological and radiological characteristics and prediction of survival in colon cancer

There are various histological characteristics which have been proposed to predict the survival rate in colon cancer. However, there is no definitive model to accurately predict the survival. Therefore, it is important to find out one model for the prediction of survival in colon cancer which may also include the preoperative, and operative factors in addition to histopathology.

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study

BACKGROUND

Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common mental disorder among older adults. This study aims to establish an effective model for predicting depression risks among disabled elderly individuals.

METHODS

The data for this study was obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS). In this study, disability was defined as a functional impairment in at least one activity of daily living (ADL) or instrumental activity of daily living (IADL). Depressive symptoms were assessed by using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D10). We employed SPSS 27.0 to select independent risk factor variables associated with depression among disabled elderly individuals. Subsequently, a predictive model for depression in this population was constructed using R 4.3.0. The model's discrimination, calibration, and clinical net benefits were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curves.

RESULTS

In this study, 3,107 elderly individuals aged 60 years and older with disabilities were included. Poor self-rated health, pain, absence of caregivers, cognitive impairment, and shorter sleep duration were identified as independent risk factors for depression in disabled elderly individuals. The XGBoost model demonstrated superior performance in the training set, while the logistic regression model outperformed it in the validation set, with AUCs of 0.76 and 0.73, respectively. The calibration curve and Brier score (Brier: 0.20) indicated a good model fit. Moreover, decision curve analysis confirmed the clinical utility of the model.

CONCLUSIONS

The predictive model exhibits outstanding predictive efficacy, greatly assisting healthcare professionals and family members in evaluating depression risks among disabled elderly individuals. Consequently, it enables the early identification of elderly individuals at high risk for depression.

Factors contributing to perioperative blood transfusion during total hip arthroplasty in patients continuing preoperative aspirin treatment: a nomogram prediction model

BACKGROUND

Total hip arthroplasty (THA) is associated with considerable blood loss during the perioperative period, which commonly requires a blood transfusion, especially in patients who continue aspirin treatment preoperatively. Blood transfusion can significantly increase both the length of hospital stay and total treatment costs and is potentially associated with adverse reactions. However, a visual predictive model for assessing the risk of blood transfusion in THA patients is lacking. The aim of this study was to develop and validate a nomogram to predict the risk of blood transfusion during THA in patients who continue aspirin treatment preoperatively.

METHODS

From June 2016 to December 2022, 228 consecutive patients who continued preoperative aspirin treatment and underwent primary unilateral THA were enrolled in this retrospective study. Potential risk factors were screened using least absolute shrinkage and selection operator (LASSO) regression, and univariate and multifactorial logistic regressions were performed on the factors screened using LASSO regression to further control for confounding effects. Finally, a nomogram was constructed on the basis of the variables identified through multiple regression analysis. Internal validation was carried out using the Bootstrap method to assess the performance of the model using the C-index, area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

Among the 228 patients, 43 (18.9%) received a blood transfusion. Patients who received a blood transfusion had a longer hospital stay (p = 0.01). The independent risk factors for blood transfusion included the concomitant use of clopidogrel (OR = 4.415), preoperative hemoglobin level (OR = 0.062), total estimated blood loss volume (OR = 3.411), American Society of Anesthesiologists (ASA) class (OR = 1.274), and the use of tranexamic acid (OR = 0.348). The prediction model had a C-index of 0.862, an internally validated C-index of 0.833, and an AUC of 0.833, indicating excellent discriminatory power. The calibration curve showed a good calibration effect, and DCA indicated that the nomogram has strong clinical applicability.

CONCLUSIONS

Based on these five independent risk factors, our nomogram can accurately predict the risk of blood transfusion in THA patients who continue aspirin treatment preoperatively, thereby assisting surgeons in clinical decision-making.

A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer

Objective

Accurate preoperative evaluation of rectal cancer lung metastases (RCLM) is critical for implementing precise medicine. While artificial intelligence (AI) methods have been successful in detecting liver and lymph node metastases using magnetic resonance (MR) images, research on lung metastases is still limited. Utilizing MR images to classify RCLM could potentially reduce ionizing radiation exposure and the costs associated with chest CT in patients without metastases. This study aims to develop and validate a transformer-based deep learning (DL) model based on pelvic MR images, integrated with clinical features, to predict RCLM.

Methods

A total of 819 patients with histologically confirmed rectal cancer who underwent preoperative pelvis MRI and carcinoembryonic antigen (CEA) tests were enrolled. Six state-of-the-art DL methods (Resnet18, EfficientNetb0, MobileNet, ShuffleNet, DenseNet, and our transformer-based model) were trained and tested on T2WI and DWI to predict RCLM. The predictive performance was assessed using the receiver operating characteristic (ROC) curve.

Results

Our transformer-based DL model achieved impressive results in the independent test set, with an AUC of 83.74% (95% CI, 72.60%-92.83%), a sensitivity of 80.00%, a specificity of 78.79%, and an accuracy of 79.01%. Specifically, for stage T4 and N2 rectal cancer cases, the model achieved AUCs of 96.67% (95% CI, 87.14%-100%, 93.33% sensitivity, 89.04% specificity, 94.74% accuracy), and 96.83% (95% CI, 88.67%-100%, 100% sensitivity, 83.33% specificity, 88.00% accuracy) respectively, in predicting RCLM. Our DL model showed a better predictive performance than other state-of-the-art DL methods.

Conclusion

The superior performance demonstrates the potential of our work for predicting RCLM, suggesting its potential assistance in personalized treatment and follow-up plans.

Overall Staging Prediction for Non-Small Cell Lung Cancer (NSCLC): A Local Pilot Study with Artificial Neural Network Approach

BACKGROUND

Non-small cell lung cancer (NSCLC) has been the most common cancer globally in the recent decade. CT is the most common imaging modality for the initial diagnosis of NSCLC. The gold standard for definitive diagnosis is the histological evaluation of a biopsy or surgical sample, which usually requires a long processing time for the confirmation of diagnosis. This study aims to develop artificial intelligence models to predict overall staging based on patient demographics and radiomics retrieved from the initial CT images, so as to prioritize later-stage patients for histology evaluation to facilitate cancer diagnosis.

METHOD

Two cohorts of NSCLC patient datasets were utilized for this study. The NSCLC-radiomics dataset from The Cancer Imaging Archive (TCIA) was divided into 70% for the training group and 30% for the internal testing group. Another cohort from a local hospital was collected for the an external testing group. Patient demographics and 107 radiomic features were retrieved from the gross tumor volume delineated by clinical oncologists on CT images. Artificial neural networks were used to build models for NSCLC overall staging (stage I, II, or III) prediction. Four traditional classifiers were also adopted to build models for comparison.

RESULT

The proposed feed-forward neural network (FFNN) model showed good performance in predicting overall staging with an accuracy of 88.84%, 76.67%, and 74.52% in overall accuracies in validation, internal cohort testing, and external cohort testing, respectively. The sensitivity and specificity are balanced in all the stages, with average precision and F1 score in each of the stages.

CONCLUSION

The FFNN demonstrated good performance in overall staging prediction for NSCLC patients. It has the benefit of predicting multiple overall stages in a single model. The software required and the proposed model are simple. It can be operated on a general-purpose computer in the radiology department. The application will eventually be used as a prediction tool to prioritize the biopsy or surgery sample for histological analysis and molecular investigation, thus shortening the time for diagnosis by pathologists, which supports the triage of patients for further testing.

Prediction of postoperative disease-free survival in colorectal cancer patients using CT radiomics nomogram: a multicenter study

BACKGROUND

Radiomics analysis is widely used to assess tumor prognosis.

PURPOSE

To explore the value of computed tomography (CT) radiomics nomogram in predicting disease-free survival (DFS) of patients with colorectal cancer (CRC) after operation.

MATERIAL AND METHODS

A total of 522 CRC patients from three centers were retrospectively included. Radiomics features were extracted from CT images, and the least absolute shrinkage and selection operator Cox regression algorithm was employed to select radiomics features. Clinical risk factors associated with DFS were selected through univariate and multivariate Cox regression analysis to build the clinical model. A predictive nomogram was developed by amalgamating pertinent clinical risk factors and radiomics features. The predictive performance of the nomogram was evaluated using the C-index, calibration curve, and decision curve. DFS probabilities were estimated using the Kaplan-Meier method.

RESULTS

Integrating the retained eight radiomics features and three clinical risk factors (pathological N stage, microsatellite instability, perineural invasion), a nomogram was constructed. The C-index for the nomogram were 0.819 (95% CI=0.794-0.844), 0.782 (95% CI=0.740-0.824), 0.786 (95% CI=0.753-0.819), and 0.803 (95% CI=0.765-0.841) in the training set, internal validation set, external validation set 1, and external validation set 2, respectively. The calibration curves demonstrated a favorable congruence between the predicted and observed values as depicted by the nomogram. The decision curve analysis underscored that the nomogram yielded a heightened clinical net benefit.

CONCLUSION

The constructed radiomics nomogram, amalgamating the radiomics features and clinical risk factors, exhibited commendable performance in the individualized prediction of postoperative DFS in CRC patients.

Development and Validation of a Nutritional Frailty Phenotype for Older Adults Based on Risk Prediction Model: Results from a Population-Based Prospective Cohort Study

OBJECTIVES

Malnutrition is generally studied to be involved in outlining hazard frailty trajectories, resulting in adverse outcomes. In view of frailty's multidimensional nature, we aimed to assess the contribution of nutritional items in existing frailty tools to adverse outcomes, and develop and validate a nutritional frailty phenotype based on machine learning.

DESIGN

A population-based prospective cohort study.

SETTING AND PARTICIPANTS

A total of 7641 older adults from the China Health and Retirement Longitudinal Study (CHARLS) were included as the training set to develop the nutritional frailty phenotype between 2011 at baseline and 2013 at follow-up, and 8656 older adults between 2013 at baseline and 2015 at follow-up were included for temporally external validation.

METHODS

The important predictors for 2-year incident adverse outcomes including all-cause mortality, disability, and combined outcomes were selected based on the least absolute shrinkage and selection operator. The nutritional frailty phenotype was developed using 2 machine learning models (random forest and eXtreme Gradient Boosting), and modified Poisson regression with the robust (sandwich) estimation of variance.

RESULTS

Slowness (walking speed), lower extremity function (chair-stand test), and upper limb function (grip strength) were selected as important predictors for each outcome using least absolute shrinkage and selection operator. For the training set, the models for predicting all-cause mortality [area under the receiver operating characteristics curves (AUCs), 0.746-0.752; mean AUCs of the 5-fold cross validation: 0.746-0.752] and combined outcome (AUCs, 0.706-0.708; mean AUCs of the 5-fold cross validation, 0.706) showed acceptable discrimination, whereas the models for predicting incident disability had approximately acceptable discrimination (AUCs, 0.681-0.683; mean AUCs of the 5-fold cross validation, 0.681-0.684). For external validation, all models had acceptable discrimination, overall prediction performance, and clinical usefulness, but only the modified Poisson regression model for predicting incident disability had acceptable calibration.

CONCLUSIONS AND IMPLICATIONS

A novel nutritional frailty phenotype may have direct implications for decreasing risk of adverse outcomes in older adults. Weakness and slowness play a major role in the progression of nutritional frailty, emphasizing that nutritional supplementation combined with exercise may be one of the feasible pathways to prevent or delay adverse outcomes.

MRI-Based Radiomics Features for Prediction of Pathological Deterioration Upgrading in Rectal Tumor

PURPOSE

Our aim is to develop and validate an MRI-based diagnostic model for predicting pathological deterioration upgrading in rectal tumor.

METHODS

This retrospective study included 158 eligible patients from January 2017 to November 2023. The patients were divided into a training group (n = 110) and a validation group (n = 48). Radiomics features were extracted from T2-weighted images to create a radiomics score model. Significant factors identified through multifactor analysis were used to develop the final clinical feature model. By combining these two models, an combined radiomics-clinical model was established. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the ROC Curve (AUC).

RESULTS

A total of 1197 features were extracted, with 11 features selected for calculating the radiomics score to establish the radiomics model. This model demonstrated good predictive performance for pathological upgrading in both the training and validation groups (AUC of 0.863 and 0.861, respectively). Clinical factors such as chief complaint and differential carcinoembryonic antigen levels showed statistical significance (P < 0.05). The clinical model, incorporating these factors, yielded AUC values of 0.669 and 0.651 for the training and validation groups, respectively. Furthermore, the radiomics-clinical combined model outperformed the individual models in predicting preoperative pathological upgrading in both the training and validation groups (AUC of 0.932 and 0.907, respectively).

CONCLUSIONS

A radiomics-clinical model, which combines clinical features with radiomics features based on MRI, can predict pathological deterioration upgrading in patients with rectal tumor and provide valuable insights for personalized treatment strategies.

Incorporating Circulating Plasma Interleukin-10 Enhanced Risk Predictability of Mortality in Acute Type A Aortic Dissection Surgery

Background

Acute type A aortic dissection (TAAD) is a life-threatening cardiovascular emergency with a high mortality rate. The peri-operative factors influencing in-hospital mortality among surgically treated TAAD patients remain unclear. This study aimed to identify key peri-operative risk factors associated with in-hospital mortality.

Methods

Peri-operative laboratory data, surgical strategies, and TAAD-related risk factors, associated with mortality, were collected. Machine learning techniques were applied to evaluate the impact of various parameters on in-hospital mortality. Based on the findings, a nomogram model was developed and validated using area under the receiver operating characteristic curve (AUC) analysis, calibration plots, and internal validation methods.

Results

A total of 199 patients with TAAD were included in the study cohort, which was divided into derivation and validation cohorts. Using the least absolute shrinkage and selection operator (LASSO) regression method, 66 features were narrowed down to six key predictors. These included age, lymphocyte count, use of continuous renal replacement therapy (CRRT), cardiopulmonary bypass (CPB) time, duration of mechanical ventilation, and postoperative interleukin-10 (IL-10) levels, all of which were identified as significant risk factors for in-hospital mortality following TAAD surgery.

Conclusions

We developed and validated a predictive model, presented as a nomogram, to estimate in-hospital survival in patients with TAAD. Post-operative IL-10 was identified as an independent prognostic factor for patients with TAAD. The combination of IL-10 with five additional indicators significantly improved the predictive accuracy, demonstrating superiority over the use of any single variable alone.

Clinical Trial Registration

This study protocol was registered at ClinicalTrials.gov (NCT04711889). https://clinicaltrials.gov/study/NCT04711889.

Development and Validation of Prediction Models for Incident Reversible Cognitive Frailty Based on Social-Ecological Predictors Using Generalized Linear Mixed Model and Machine Learning Algorithms: A Prospective Cohort Study

This study aimed to develop and validate prediction models for incident reversible cognitive frailty (RCF) based on social-ecological predictors. Older adults aged ≥60 years from China Health and Retirement Longitudinal Study (CHARLS) 2011-2013 survey were included as training set (n = 1230). The generalized linear mixed model (GLMM), eXtreme Gradient Boosting, support vector machine, random forest, and Binary Mixed Model forest were used to develop prediction models. All models were evaluated internally with 5-fold cross-validation and evaluated externally via CHARLS 2013-2015 survey (n = 1631). Only GLMM showed good discrimination (AUC = 0.765, 95% CI = 0.736, 0.795) in training set, and all models showed fair discrimination (AUC = 0.578-0.667, 95% CI = 0.545, 0.725) in internal and external validation. All models showed acceptable calibration, overall prediction performance, and clinical usefulness in training and validation sets. Older adults were divided into three groups using risk score based on GLMM, which could assist healthcare providers to predict incident RCF, facilitating early identification of high-risk population.

MRI-based multiregional radiomics for desmoplastic reaction classification and prognosis stratification in stage II rectal cancer: A bicenter study

PURPOSE

To develop an MRI-based multiregional radiomics model for the noninvasive desmoplastic reaction (DR) classification and prognosis stratification in stage II rectal cancer (RC) patients.

MATERIALS AND METHODS

This study retrospectively involved 336 patients with RC from two centers, with 239 from Center 1 divided into training (n = 191) and internal validation (n = 48) datasets at an 8:2 ratio, and 97 from Center 2 serving as external validation dataset. Radiomics features were extracted, and a multiregional radiomics DR (M-RDR) signature was established using multi-level feature selection procedure. The cut-off value for M-RDR was determined using Youden's index. We further evaluated the predictive values of M-RDR on prognosis and adjuvant chemotherapy stratification. The primary outcome was 3-year disease-free survival (DFS), and cox model performance was assessed using AUCs and 95 % confidence intervals.

RESULTS

M-RDR demonstrated a high accuracy in DR classification with AUCs of 0.778 and 0.798 in the training and internal validation datasets. Multivariable analysis confirmed M-RDR as an independent prognostic factor after adjusting for clinicopathological factors.The combined model incorporating M-RDR and clinicopathological factors showed good performance in predicting 3-year DFS, with AUCs of 0.923, 0.908, and 0.891 in the training, internal validation and external validation datasets, respectively. Additionally, patients in the M-RDR-high group who received adjuvant chemotherapy had significantly better DFS compared with those who did not (P < 0.05).

CONCLUSION

The MRI-based multiregional radiomics model could effectively improve non-invasive DR classification, and was able to enhance postoperative risk stratification and treatment decision-making in stage II RC patients.

Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature

BACKGROUND

The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed.

RESULTS

A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767-0.900) for the training cohort and 0.733 (95% CI, 0.574-0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761-0.899) in the training cohort and 0.793 (95% CI, 0.653-0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795-0.928) in the training cohort and 0. 837 (95% CI, 0.705-0.969) in the test cohort.

CONCLUSION

An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.

Development and validation of a recurrence risk prediction model for elderly schizophrenia patients

OBJECTIVE

To construct a recurrence prediction model for Elderly Schizophrenia Patients (ESCZP) and validate the model's spatial external applicability.

METHODS

The modeling cohort consisted of 365 ESCZP cases from the Seventh People's Hospital of Dalian, admitted between May 2022 and April 2024. Variables were selected using Lasso-Logistic regression to construct the recurrence prediction model, with a nomogram plotted using the "RMS" package in R 4.3.3 software. Model validation was performed using 1,000 bootstrap resamples. Spatial external validation was conducted using 172 cases ESCZP from the Fourth Affiliated Hospital of Qiqihar Medical College during the same period. The model's discrimination, accuracy, and clinical utility were assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). The Hosmer-Lemeshow test was used to evaluate model fit.

RESULTS

A total of 537 cases ESCZP were included based on inclusion and exclusion criteria, with 150 recurrences within two years and 387 non-recurrences. Lasso-Logistic regression analysis identified Medication status, Premorbid personality, Exercise frequency, Drug adverse reactions, Family care, Social support, and Life events as predictors of ESCZP recurrence. The AUC for the modeling cohort was 0.877 (95% CI: 0.837-0.917). For the external validation cohort, the AUC was 0.838 (95% CI: 0.776-0.899). Calibration curves indicated that the fit was close to the reference line, demonstrating high model stability. DCA results showed good net benefit at a threshold probability of 80%.

CONCLUSION

The nomogram prediction model developed based on Lasso-Logistic regression shows potential in identifying the risk of recurrence in ESCZP. However, further validation and refinement are needed before it can be applied in routine clinical practice.

A Radiomic and Clinical Data-Based Risk Model for Malignancy Prediction of Breast BI-RADS 4A Microcalcifications

BACKGROUND

Mammography is the gold standard technique for early breast cancer screening, but it has a limited specificity for microcalcifications. Radiomics represents a promising tool for enhancing lesion risk stratification. This study aims to evaluate the reliability of radiomics in combination with clinical data to classify benign and malignant microcalcifications, potentially enhancing the standard radiological assessment and reducing the need for biopsies.

MATERIALS AND METHODS

This study retrospectively analyzed patients with BI-RADS 4A microcalcifications who underwent mammography (MX) and vacuum-assisted breast biopsy (VABB) at our center from January 2019 to February 2023. About 104 radiomics features were extracted from a region of interest, manually defined on images. Clinical data from each patient were collected. Using the Tyrer-Cuzick Model, we classified patients according to the risk of developing breast cancer. Two logistic regression models, using clinical and radiomics data were trained to predict the pathological classification of breast calcifications.

RESULTS

A total of 167 calcification groups were included in the study. The final dataset was made of 14 radiomics features. The radiomics model alone achieved an AUC of 0.72 (95% CI, 0.61-0.33) while the model trained on clinical and radiomics features obtained AUC values of 0.81 (95% CI, 0.69-0.92).

CONCLUSIONS

Our findings suggest that the integration of clinical data with radiomics has the potential to reduce unnecessary biopsies for BI-RADS 4A microcalcifications, leading to more targeted and personalized patient care.

Diameter thresholds for pure ground-glass pulmonary nodules at low-dose CT screening: Chinese experience

BACKGROUND

Limited research exists on screening thresholds for low-dose CT in detecting malignant pure ground-glass lung nodules (pGGNs) in the Chinese population.

MATERIALS AND METHODS

A retrospective analysis of the Guangzhou Lung-Care programme was conducted, retrieving average transverse diameter, location, histopathology, frequency and follow-up intervals. Diagnostic performances for 'lung cancers' were evaluated using areas under the curve (AUCs), decision curve analysis (DCA), sensitivities and specificities, with thresholds ranging from 5 mm to 10 mm. We divide malignant pGGNs into three groups: (1) minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA), (2) atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) and MIA and IA and (3) IA-only.

RESULTS

In 'MIA+IA', increasing the threshold from 5 mm to 8 mm improved specificity (60.97% to 88.85%, p<0.001) and positive predictive values (PPVs; 5.87% to 14.88%, p<0.001), but decreased sensitivity (94.44% to 75.56%, p<0.001). Further raising threshold from 8 mm reduced sensitivity (75.56% to 60.00%, p<0.001), while slightly increasing specificity (88.85% to 93.47%, p<0.001) and PPVs (14.88% to 19.15%, p<0.001). Increasing threshold from 5 mm to 7 mm enhanced the AUC for 'MIA+IA' (from 0.711 to 0.829), 'AAH+AIS+MIA+IA' (from 0.748 to 0.804) and 'IA-only' (from 0.783 to 0.833). At 8 mm, the AUCs for these categories were similar. However, increasing the threshold from 7 mm to 10 mm resulted in reduced AUCs for 'MIA+IA' (0.829 to 0.767), 'AAH+AIS+MIA+IA' (0.804 to 0.744) and 'IA-only' (0.833 to 0.800). DCA reveals that the 8 mm predictive model demonstrates greater clinical utility compared with models with other thresholds.

CONCLUSIONS

Increasing the diameter threshold for positive results for pGGNs, up to 8 mm could enhance diagnostic performance.

TRIAL REGISTRATION NUMBER

NCT04938804.

Radiomics in rectal cancer: current status of use and advances in research

Rectal cancer is a leading cause of morbidity and mortality among patients with malignant tumors in China. In light of the advances made in therapeutic approaches such as neoadjuvant therapy and total mesorectal excision, precise preoperative assessment has become crucial for developing a personalized treatment plan. As an emerging technology, radiomics has gained widespread application in the diagnosis, assessment of treatment response, and analysis of prognosis for rectal cancer by extracting high-throughput quantitative features from medical images. Radiomics thus demonstrates considerable potential for optimizing clinical decision-making. In this paper, we reviewed recent research focusing on advances in the use of radiomics for managing rectal cancer. The review covers TNM staging of tumors, assessment of neoadjuvant therapy outcomes, and survival prediction. We also discuss the challenges and prospects for future developments in translational medicine, particularly the need for data standardization, consistent feature extraction methodologies, and rigorous model validation.

Identifying risk factors and constructing a predictive model for heart failure combined with intracardiac thrombus in non-compaction cardiomyopathy patients

This study aims to develop a nomogram prediction model for assessing the cardiogenic composite endpoint, which includes intracardiac thrombosis (ICT) combined with heart failure (HF) in patients with non-compaction cardiomyopathy (NCM) patients. We retrospectively analyzed clinical data from NCM patients (January 2018 to May 2024), who were randomly assigned to training and validation cohorts. Independent predictors were identified using logistic regression, and a nomogram model was developed. The model's discriminative ability, accuracy, and clinical applicability were subsequently validated. A total of 976 patients were included, of whom 54 had ICT and 191 had HF. Diabetes mellitus (DM), left ventricular end-systolic diameter (LVESD), and ejection fraction (EF) were identified as independent predictors for the composite endpoint. The nomogram demonstrated good performance, with an area under the curve (AUC) of 0.747 (95% CI: 0.707-0.787) in the training group and 0.803 (95% CI: 0.752-0.854) in the validation group. The calibration curve for the training group showed an average absolute error of 0.028, with a Hosmer-Lemeshow test P-value of 0.076. Decision curve analysis and clinical impact curves further indicated that the clinical net benefit was maximized at a threshold probability of 0.05-0.61. This study establishes and validates a nomogram for predicting cardiogenic composite endpoint in NVM patients, demonstrating robust clinical predictive value.

Magnetic resonance imaging radiomics based on artificial intelligence is helpful to evaluate the prognosis of single hepatocellular carcinoma

Background

Previous studies mostly use single-type features to establish a prediction model. We aim to develop a comprehensive prediction model that effectively identify patients with poor prognosis for single hepatocellular carcinoma (HCC) based on artificial intelligence (AI). Patients and methods: 236 single HCC patients were studied to establish a comprehensive prediction model. We collected the basic information of patients and used AI to extract the features of magnetic resonance (MR) images.

Results

The clinical model based on linear regression (LR) algorithm (AUC: 0.658, 95%CI: 0.5021-0.8137), the radiomics model and deep transfer learning (DTL) model based on light gradient-boosting machine (Light GBM) algorithm (AUC: 0.761, 95%CI: 0.6326-0.8886 and AUC: 0.784, 95%CI: 0.6587-0.9087, respectively) were the optimal prediction models. A comparison revealed that the integrated nomogram had the largest area under the receiver operating characteristic curve (AUC) (all P < 0.05). In the training cohort, the integrated nomogram was predictive of recurrence-free survival (RFS) as well as overall survival (OS) (C-index: 0.735 and 0.712, P < 0.001). In the test cohort, the integrated nomogram also can predict RFS and OS (C-index: 0.718 and 0.740, P < 0.001) in patients.

Conclusion

The integrated nomogram composed of signatures in the prediction models can not only predict the postoperative recurrence of single HCC patients but also stratify the risk of OS after the operation.

Development and validation of a predictive model and tool for functional recovery in patients after postero-lateral interbody fusion

OBJECTIVE

The postoperative recovery of patients with lumbar disc herniation (LDH) requires further study. This study aimed to establish and validate a predictive model for functional recovery in patients with LDH and explore associated risk factors.

METHOD

Patients with LDH undergoing PLIF admitted from January 1, 2018 to December 31, 2022 were included, and patient data were prospectively collected through follow-up. The training and validation cohorts were randomly assigned in a 7:3 ratio. To pool data variables LASSO regression was used. The pooled variables were subsequently included in binary logistic regression analyses, construct risk prediction models, and plot nomograms. Additionally, recovery prediction models and interactive web page calculators were developed using R Shiny.

RESULTS

Overall, 1,097 patients with LDH following PLIF were included in this study. Regarding patients' economic and functional scores, 927 (84.5%) received excellent scores. Key indicators significantly were screened. Multivariate analysis showed that age, season, occupation, HDL-C, smoking, weekly exercise time, and osteoporosis were independent risk factors for postoperative recovery. The C-index of the model was 0.776 (95% CI: 0.7312-0.8208) and 0.804 (95% CI: 0.7408-0.8673) for the training and validation cohorts, respectively. The H-L test showed good fitting of the model (all P > 0.05). The DCA curve showed the best clinical efficacy when the threshold probability was in the ranges of 0-0.71 and 0.79-0.84. The interactive web calculator is accessed at https://postoperativerecoveryofldh.shinyapps.io/DynNomapp/ .

CONCLUSION

The predictive tools derived from this study can provide realistic and personalized expectations of postoperative outcomes for patients undergoing lumbar spine surgery.

Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics

Objective

The invasiveness of pituitary neuroendocrine tumor is an important basis for formulating individualized treatment plans and improving the prognosis of patients. Radiomics can predict invasiveness preoperatively. To investigate the value of multiparameter magnetic resonance imaging (mpMRI) radiomics in predicting pituitary neuroendocrine tumor invasion into the cavernous sinus (CS) before surgery.

Patients and methods

The clinical data of 133 patients with pituitary neuroendocrine tumor (62 invasive and 71 non-invasive) confirmed by surgery and pathology who underwent preoperative mpMRI examination were retrospectively analyzed. Data were divided into training set and testing set according to different field strength equipment. Radiomics features were extracted from the manually delineated regions of interest in T1WI, T2WI and CE-T1, and the best radiomics features were screened by LASSO algorithm. Single radiomics model (T1WI, T2WI, CE-T1) and combined radiomics model (T1WI+T2WI+CE-T1) were constructed respectively. In addition, clinical features were screened to establish clinical model. Finally, the prediction model was evaluated by ROC curve, calibration curve and decision curve analysis (DCA).

Results

A total of 10 radiomics features were selected from 306 primitive features. The combined radiomics model had the highest prediction efficiency. The area under curve (AUC) of the training set was 0.885 (95% CI, 0.819-0.952), and the accuracy, sensitivity, and specificity were 0.951,0.826, and 0.725. The AUC of the testing set was 0.864 (95% CI, 0.744-0.985), and the accuracy, sensitivity, and specificity were 0.829,0.952, and 0.700. DCA showed that the combined radiomics model had higher clinical net benefit.

Conclusion

The combined radiomics model based on mpMRI can effectively and accurately predict the invasiveness of pituitary neuroendocrine tumor to CS preoperatively, and provide decision-making basis for clinical individualized treatment.

Development of a Prognostic Nomogram Incorporating the Naples Prognostic Score for Postoperative Oral Squamous Cell Carcinoma Patients

Background

The Naples prognostic score (NPS) and its relation to the prognosis of oral squamous cell carcinoma (OSCC) have been inconclusive. This study aimed to investigate the correlation between NPS and the prognosis of postoperative OSCC patients. Additionally, the study sought to develop a new nomogram for predicting disease-free survival (DFS) and overall survival (OS).

Methods

The study included 576 OSCC patients who underwent surgical treatment at two hospitals between August 2008 and June 2018. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors. Subsequently, two nomograms were developed to predict DFS and OS based on these factors and underwent rigorous validation.

Results

The median DFS and OS were 31.5 months and 36.5 months, respectively. Significant differences in DFS and OS were observed among patients with different NPS scores. Adjuvant radiotherapy, age-adjusted Charlson comorbidity index (ACCI), extranodal extension (ENE), NPS, American Joint Committee on Cancer (AJCC) stage, surgical safety margin, eastern cooperative oncology group performance status (ECOG PS), and systemic inflammation score (SIS) were identified as independent predictors of DFS and OS. In the training cohort, the nomogram's concordance index (C-index) for predicting DFS and OS was 0.701 and 0.693, respectively. In the validation group, the corresponding values were 0.642 and 0.635, respectively. Calibration plots confirmed a high level of agreement between the model's predictions and actual outcomes. Decision curve analysis (DCA) demonstrated the nomogram's good clinical utility. Additionally, patients in the low-risk group did not benefit from adjuvant radiotherapy, while those in the medium-risk and high-risk group could benefit from adjuvant radiotherapy.

Conclusion

NPS significantly influences the prognosis of OSCC patients following surgery. The nomogram developed in this study holds significant clinical application potential. The low-risk subgroup of patients was not required to undergo postoperative radiotherapy.

Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics

OBJECTIVES

This study aims to evaluate image quality and regional lymph node metastasis (LNM) in patients with rectal cancer (RC) on multi-b-value diffusion-weighted imaging (DWI).

METHODS

This retrospective study included 199 patients with RC who had undergone multi-b-value DWI. Subjective (five-point Likert scale) and objective assessments of quality images were performed on DWIb1000, DWIb2000, and DWIb3000. Patients were randomly divided into a training (n = 140) or validation cohort (n = 59). Radiomics features were extracted within the whole volume tumor on ADC maps (b = 0, 1000 s/mm2), DWIb1000, DWIb2000, and DWIb3000, respectively. Five prediction models based on selected features were developed using logistic regression analysis. The performance of radiomics models was evaluated with a receiver operating characteristic curve, calibration, and decision curve analysis (DCA).

RESULTS

The mean signal intensity of the tumor (SItumor), signal-to-noise ratio (SNR), and artifact and anatomic differentiability score gradually were decreased as the b-value increased. However, the contrast-to-noise (CNR) on DWIb2000 was superior to those of DWIb1000 and DWIb3000 (4.58 ± 0.86, 3.82 ± 0.77, 4.18 ± 0.84, p < 0.001, respectively). The overall image quality score of DWIb2000 was higher than that of DWIb3000 (p < 0.001) and showed no significant difference between DWIb1000 and DWIb2000 (p = 0.059). The area under curve (AUC) value of the radiomics model based on DWIb2000 (0.728) was higher than conventional ADC maps (0.690), DWIb1000 (0.699), and DWIb3000 (0.707), but inferior to multi-b-value DWI (0.739) in predicting LNM.

CONCLUSION

DWIb2000 provides better lesion conspicuity and LNM prediction than DWIb1000 and DWIb3000 in RC.

CLINICAL RELEVANCE STATEMENT

DWIb2000 offers satisfactory visualization of lesions. Radiomics features based on DWIb2000 can be applied for preoperatively predicting regional lymph node metastasis in rectal cancer, thereby benefiting the stratified treatment strategy.

KEY POINTS

Lymph node staging is required to determine the best treatment plan for rectal cancer. DWIb2000 provides superior contrast-to-noise ratio and lesion conspicuity and its derived radiomics best predict lymph node metastasis. DWIb2000 may be recommended as the optimal b-value in rectal MRI protocol.

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicentre study

OBJECTIVES

Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics model for the prediction of LNM in OPSCCs using contrast-enhanced computed tomography (CECT).

METHODS

A retrospective analysis included 279 OPSCC patients from 3 institutions. CECT images were used for handcrafted (HCR) and DL feature extraction. Dimensionality reduction for HCR features used recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) algorithms, whereas DL feature dimensionality reduction used variance-threshold and RFE algorithms. Radiomics signatures were constructed using six machine learning classifiers. A combined model was then constructed using the screened DL, HCR, and clinical features. The area under the receiver operating characteristic curve (AUC) served to quantify the model's performance, and calibration curves were utilized to assess its calibration.

RESULTS

The combined model exhibited robust performance, achieving AUC values of 0.909 (95% CI, 0.861-0.957) in the training cohort, 0.884 (95% CI, 0.800-0.968) in the internal validation cohort, and 0.865 (95% CI, 0.791-0.939) in the external validation cohort. It outperformed both the clinical model and best-performing radiomics model. Moreover, calibration was deemed satisfactory.

CONCLUSIONS

The combined model based on CECT demonstrates the potential to predict LNM in OPSCCs preoperatively, offering a valuable tool for more precise and tailored treatment strategies.

ADVANCES IN KNOWLEDGE

This study presents a novel combined model integrating clinical factors with DL radiomics, significantly enhancing preoperative LNM prediction in OPSCC.

A 18F-FDG PET/CT based radiomics nomogram for predicting disease-free survival in stage II/III colorectal adenocarcinoma

OBJECTIVES

This study aimed to establish a clinical nomogram model based on a radiomics signatures derived from 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET/CT) and clinical parameters to predict disease-free survival (DFS) in patients with stage II/III colorectal adenocarcinoma. Understanding and predicting DFS in these patients is key to optimizing treatment strategies.

METHODS

A retrospective analysis included 332 cases from July 2011 to July 2021 at The Sixth Affiliated Hospital, Sun Yat-sen University, with PET/CT assessing radiomics features and clinicopathological features. Univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox, and multivariable Cox regression identified recurrence-related radiomics features. We used a weighted radiomics score (Rad-score) and independent risk factors to construct a nomogram. Evaluation involved time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The nomogram, incorporating Rad-score, pN, and pT demonstrated robust predictive ability for DFS in stage II/III colorectal adenocarcinoma. Training cohort areas under the curve (AUCs) were 0.78, 0.80, and 0.86 at 1, 2, and 3 years, respectively, and validation cohort AUCs were 0.79, 0.75, and 0.73. DCA and calibration curves affirmed the nomogram's clinical relevance.

CONCLUSION

The 18F-FDG PET/CT based radiomics nomogram, including Rad-score, pN, and pT, effectively predicted tumor recurrence in stage II/III colorectal adenocarcinoma, significantly enhancing prognostic stratification. Our findings highlight the potential of this nomogram as a guide for clinical decision making to improve patient outcomes.

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer

OBJECTIVE

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy.

RESULTS

The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).

CONCLUSION

Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.

CLINICAL RELEVANCE STATEMENT

The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.

KEY POINTS

Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

Radiomic nomograms in CT diagnosis of gall bladder carcinoma: a narrative review

Radiomics is a method that extracts many features from medical images using various algorithms. Medical nomograms are graphical representations of statistical predictive models that produce a likelihood of a clinical event for a specific individual based on biological and clinical data. The radiomic nomogram was first introduced in 2016 to study the integration of specific radiomic characteristics with clinically significant risk factors for patients with colorectal cancer lymph node metastases. Thereby it gained momentum and made its way into different domains of breast, liver, and head and neck cancer. Deep learning-based radiomics which automatically generates and extracts significant features from the input data using various neural network architectures, along with the generation and usage of nomograms are the latest developments in the application of radiomics for the diagnosis of gall bladder carcinoma. Although radiomics has demonstrated encouraging outcomes in the diagnosis of gall bladder carcinoma, but most of the studies conducted suffer from a lack of external validation cohorts, smaller sample sizes, and paucity of prospective utility in routine clinical settings.

An integrative nomogram based on MRI radiomics and clinical characteristics for prognosis prediction in cervical spinal cord Injury

OBJECTIVE

To construct a nomogram model based on magnetic resonance imaging (MRI) radiomics combined with clinical characteristics and evaluate its role and value in predicting the prognosis of patients with cervical spinal cord injury (cSCI).

METHODS

In this study, we assessed the prognosis of 168 cSCI patients using the American Spinal Injury Association (ASIA) scale and the Functional Independence Measure (FIM) scale. The study involved extracting radiomics features using both manually defined metrics and features derived through deep learning via transfer learning methods from MRI sequences, specifically T1-weighted and T2-weighted images (T1WI & T2WI). The feature selection was performed employing the least absolute shrinkage and selection operator (Lasso) regression across both radiomics and deep transfer learning datasets. Following this selection process, a deep learning radiomics signature was established. This signature, in conjunction with clinical data, was incorporated into a predictive model. The efficacy of the models was appraised using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) to assess their diagnostic performance.

RESULTS

Comparing the effectiveness of the models by linking the AUC of each model, we chose the best-performance radiomics model with clinical model to create the final nomogram. Our analysis revealed that, in the testing cohort, the combined model achieved an AUC of 0.979 for the ASIA and 0.947 for the FIM. The training cohort showed more promising performance, with an AUC of 0.957 for ASIA and 1.000 for FIM. Furthermore, the calibration curve showed that the predicted probability of the nomogram was consistent with the actual incidence rate and the DCA curve validated its effectiveness as a prognostic tool in a clinical setting.

CONCLUSION

We constructed a combined model that can be used to help predict the prognosis of cSCI patients with radiomics and clinical characteristics, and further provided guidance for clinical decision-making by generating a nomogram.

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study

RATIONALE AND OBJECTIVES

To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.

MATERIALS AND METHODS

This retrospective study involved 286 cancer patients confirmed by histopathology from center 1 (Training set) and 66 patients from center 2 (External test set). Radiomics features were extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, whereas DL features were obtained using four models: MobileNet-V3-large, Inception-V3, ResNet50, and VGG16. These DL radiomics (DLR) features were then combined to construct a machine learning model. The Shapley additive interpretation (SHAP) tool was utilized to investigate the interpretability of the model. We evaluated and compared the diagnostic performance of senior and junior radiologists, with and without the aid of the optimal DLR model. Kaplan-Meier survival curve was used to analyze the prognosis of patients.

RESULTS

The DLR model outperforms individual DL models and the radiomics model. The MobileNet-V3-large combination radiomics signature demonstrated the best performance, achieving an AUC of 0.878 on the Training set and 0.752 on the External test set. Compared to the traditional radiomics model, the AUC for the Training set increased by 0.094 and by 0.051 for the External test set. This model facilitated improved diagnostic performance among both junior and senior radiologists. Specifically, the AUC values for junior and senior radiologists increased by 0.162 and 0.232, respectively, on the Training set; and by 0.096 and 0.113, respectively, on the External test set. The DLR model demonstrated strong performance in risk stratification for disease-free survival.

CONCLUSION

The DLR model developed from multiparametric MRI can effectively distinguish cancer LN metastasis and enhance radiologists' diagnostic performance.

Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels

Background

Approximately 60% of Asian populations with non-small cell lung cancer (NSCLC) harbor epidermal growth factor receptor (EGFR) gene mutations, marking it as a pivotal target for genotype-directed therapies. Currently, determining EGFR mutation status relies on DNA sequencing of histological or cytological specimens. This study presents a predictive model integrating clinical parameters, computed tomography (CT) characteristics, and serum tumor markers to forecast EGFR mutation status in NSCLC patients.

Methods

Retrospective data collection was conducted on NSCLC patients diagnosed between January 2018 and June 2019 at the First Affiliated Hospital of Zhengzhou University, with available molecular pathology results. Clinical information, CT imaging features, and serum tumor marker levels were compiled. Four distinct models were employed in constructing the diagnostic model. Model diagnostic efficacy was assessed through receiver operating characteristic (ROC) area under the curve (AUC) values and calibration curves. DeLong's test was administered to validate model robustness.

Results

Our study encompassed 748 participants. Logistic regression modeling, trained with the aforementioned variables, demonstrated remarkable predictive capability, achieving an AUC of 0.805 (95% confidence interval (CI) [0.766-0.844]) in the primary cohort and 0.753 (95% CI [0.687-0.818]) in the validation cohort. Calibration plots suggested a favorable fit of the model to the data.

Conclusions

The developed logistic regression model emerges as a promising tool for forecasting EGFR mutation status. It holds potential to aid clinicians in more precisely identifying patients likely to benefit from EGFR molecular testing and facilitating targeted therapy decision-making, particularly in scenarios where molecular testing is impractical or inaccessible.

Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer

RATIONALE AND OBJECTIVES

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics had been used to evaluate lymphovascular invasion (LVI) in patients with breast cancer. However, no studies had explored the associations between features from delayed phase as well as multiphases DCE-MRI and the LVI status. Thus, we aimed to develop an efficient nomogram based on multiphases DCE-MRI to predict the LVI status in invasive (IBC) breast cancer patients.

MATERIALS AND METHODS

A retrospective analysis was conducted on preoperative clinical, pathological, and DCE-MRI data of 173 breast cancer patients. All patients were randomly assigned into training set (n=121) and validation set (n=52) in 7:3 ratio. The clinical, pathologic, and conventional MRI characteristics were then subjected to univariate and multivariate logistic regression analysis, and the clinical risk factors with P < 0.05 in the multivariate logistic regression were used to build clinical models. Different single-phase models (early phase, peak phase, and terminal phase), as well as a multiphases model integrating radiomics features from multiple phases, were established. Furthermore, a preoperative radiomics nomogram model was constructed by combining the rad-score of the multiphases model with clinicopathologic independent risk factors. Finally, the performance of the multiphases model, clinical model, and rad-score was compared using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and decision curve analysis (DCA). The clinical utility of the rad-score was evaluated using calibration curves, and Delong test was used to compare the differences in AUC values among the different models.

RESULTS

The axillary lymph nodes (ALN) status and Ki-67 had been identified as clinicopathologic independent predictors and a clinical model had been constructed. Image features that were extracted from the terminal phase of the DCE-MRI exhibited notably superior predictive performances compared to features from the other single phases. Particularly, in the multiphases model, terminal phase features were identified as potentially providing more predictive information. Among the nine features that were found to be associated with LVI in the multiphase model, one was derived from the early phase, two from the peak phase, and six from the terminal phase, indicating that terminal phase features contributed significantly more information towards predicting LVI. Evaluation of the nomogram performance revealed promising results in both the training set (AUCs: clinical model vs. multiphase model vs. nomogram=0.734 vs. 0.840 vs. 0.876) and the validation set (AUCs: clinical model vs. multiphase model vs. nomogram=0.765 vs. 0.753 vs. 0.832).

CONCLUSION

The DCE-MRI-based radiomics model demonstrated utility in predicting LVI status, features of the terminal phase offered more valuable information particularly. The preoperative radiomics nomogram enhanced the diagnostic capability of identifying LVI status in IBC patients, and might aid clinicians in making personalized treatment decisions.

Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform

BACKGROUND

Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity.

METHODS

We retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961).

RESULTS

A total of 301 patients were enrolled. They were randomly assigned into the training (n = 210) and validation (n = 91) cohorts at a ratio of 7:3. The random forest algorithm identified the level of calcium ions, WBC count, urea level, combined cholecystitis, gallbladder wall thickening, gallstones, and hydrothorax as the seven predictive factors for severity of GSP. In the validation cohort, the areas under the curve for the radiomics model and ML GSP model were 0.841 (0.757-0.926) and 0.914 (0.851-0.978), respectively. The calibration plot shows that the ML GSP model has good consistency between the prediction probability and the observation probability. Decision curve analysis showed that the ML GSP model had high clinical utility.

CONCLUSIONS

We built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.

Recent advances in algorithms predicting hemodynamic instability undergoing surgery for phaeochromocytoma and paraganglioma

Abdominal pheochromocytomas and paragangliomas (PPGLs) are characterized by the overproduction of catecholamines, which are associated with hemodynamic instability (HDI) during surgery. Therefore, perioperative management to prevent intraoperative HDI is imperative for the surgical treatment of PPGLs. Owing to the rarity and heterogeneous nature of these tumors, pre-surgical prediction of HDI is a clinical dilemma. The reported risk factors for HDI include perioperative preparation, genetic background, tumor conditions, body composition, catecholamine levels, and surgical approach. Additionally, several personalized algorithms or models including these factors have been developed. The first part of this review outlines the prediction models that include clinical features such as tumor size and location, body mass index (BMI), blood glucose level, catecholamine levels, and preoperative management with α-adrenoceptor blockade and crystal/colloid fluid. We then summarize recently reported models that consider additional factors such as genetic background, radiomics, robotic-assisted surgical approach, three-dimensional visualization, and machine-learning models. These findings suggest that a comprehensive model including risk factors is the most likely approach for achieving effective perioperative management.

Predicting and Analyzing Restenosis Risk after Endovascular Treatment in Lower Extremity Arterial Disease: Development and Assessment of a Predictive Nomogram

PURPOSE

This study aimed to develop and internally validate nomograms for predicting restenosis after endovascular treatment of lower extremity arterial diseases.

MATERIALS AND METHODS

A total of 181 hospitalized patients with lower extremity arterial disease diagnosed for the first time between 2018 and 2019 were retrospectively collected. Patients were randomly divided into a primary cohort (n=127) and a validation cohort (n=54) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to optimize the feature selection of the prediction model. Combined with the best characteristics of LASSO regression, the prediction model was established by multivariate Cox regression analysis. The predictive models' identification, calibration, and clinical practicability were evaluated by the C index, calibration curve, and decision curve. The prognosis of patients with different grades was compared by survival analysis. Internal validation of the model used data from the validation cohort.

RESULTS

The predictive factors included in the nomogram were lesion site, use of antiplatelet drugs, application of drug coating technology, calibration, coronary heart disease, and international normalized ratio (INR). The prediction model demonstrated good calibration ability, and the C index was 0.762 (95% confidence interval: 0.691-0.823). The C index of the validation cohort was 0.864 (95% confidence interval: 0.801-0.927), which also showed good calibration ability. The decision curve shows that when the threshold probability of the prediction model is more significant than 2.5%, the patients benefit significantly from our prediction model, and the maximum net benefit rate is 30.9%. Patients were graded according to the nomogram. Survival analysis found that there was a significant difference in the postoperative primary patency rate between patients of different classifications (log-rank p<0.001) in both the primary cohort and the validation cohort.

CONCLUSION

We developed a nomogram to predict the risk of target vessel restenosis after endovascular treatment by considering information on lesion site, postoperative antiplatelet drugs, calcification, coronary heart disease, drug coating technology, and INR.

CLINICAL IMPACT

Clinicians can grade patients after endovascular procedure according to the scores of the nomograms and apply intervention measures of different intensities for people at different risk levels. During the follow-up process, an individualized follow-up plan can be further formulated according to the risk classification. Identifying and analyzing risk factors is essential for making appropriate clinical decisions to prevent restenosis.

Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion

OBJECTIVE

Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomics model for distinguishing scTDL from IMSCT before treatment initiation.

METHODS

A total of 75 patients were analyzed in this retrospective study, comprising 55 with IMSCT and 20 with scTDL. Radiomics features were extracted from T1- and T2-weighted imaging (T1&T2WI) scans upon admission. Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). The performance of the optimal model was then compared to radiologists' assessments.

RESULTS

This study developed 30 predictive models using ten classifiers across two imaging sequences. The MLP model with two sequences (T1&T2WI) emerged as the most effective one, showing superior accuracy in MRI analysis with an area under the curve (AUC) of 0.991 in training and 0.962 in testing. Moreover, statistical analyses highlighted the radiomics model significantly outperformed radiologists' assessments (p < 0.05) in distinguishing between IMSCT and scTDL.

CONCLUSION

We present an MRI-based radiomics model with high diagnostic accuracy in differentiating IMSCT from scTDL. The model's performance was comparable to junior radiologists, highlighting its potential as an effective diagnostic aid in clinical practice.

Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features

Purpose

This study aimed to develop a model to predict the risk of axillary lymph node (ALN) metastasis in breast cancer patients, using gray-scale ultrasound and clinical pathological features.

Methods

A retrospective analysis of 212 breast cancer patients who met the inclusion criteria from January 2011 to December 2021 was carried out. Clinical and pathological characteristics, including age, tumor size, pathological type, molecular subtype, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and proliferation cell nuclear antigen (Ki-67), were examined. Preoperative ultrasound examinations were performed, and ultrasound radiomics features of breast cancer lesions were extracted using Pyradiomics software. The data was divided into training (70%) and testing (30%) sets. A predictive model for axillary lymph node metastasis (ALNM) was established by combining clinical and ultrasound features. The diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curves and five-fold cross-validation.

Results

The rate of lymph node metastasis was 41.51%. Using LASSO algorithm, 17 features linked to ALN metastasis were extracted from a comprehensive databank of 8 clinical features and 1314 ultrasound radiomic attributes. Of these, four were clinical-pathological features (tumor size, tumor type, age, and expression levels of the Ki-67 protein), and 13 were radiomic features. And the following features exhibited both high weights and correlation coefficients: tumor size (R=0.29, weight=0.071), tumor type (R=-0.24, weight=-0.048), wavelet-LH_glcm_Imc1 (R=0.28, weight=0.029363), wavelet-LH_glszm_SZNUN (R=-0.20, weight=-0.028507), and squareroot_ firstorder_ Minimum (R= -0.25, weight= -0.059). The ROC area under the curve for the model in the training and testing sets was 0.882 (95% CI: 0.830-0.935) and 0.853 (95% CI: 0.762-0.945), respectively. The predictive model demonstrated a sensitivity of 87.5% on the training set and 79.2% on the test set, with corresponding specificities of 75.0% and 77.5%, accuracy of 80.4% and 78.1%, respectively. When evaluated using 5-fold cross-validation, the model achieved an average test set area under the curve (AUC) of 0.799 and a training set AUC of 0.852.

Conclusion

The clinical-radiomic model has the potential to predict axillary lymph node metastasis in breast cancer.

Visualization analysis of research hotspots and trends in MRI-based artificial intelligence in rectal cancer

Background

Rectal cancer (RC) is one of the most common types of cancer worldwide. With the development of artificial intelligence (AI), the application of AI in preoperative evaluation and follow-up treatment of RC based on magnetic resonance imaging (MRI) has been the focus of research in this field. This review was conducted to develop comprehensive insight into the current research progress, hotspots, and future trends in AI based on MRI in RC, which remains to be studied.

Methods

Literature related to AI based on MRI and RC, as of November 2023, was obtained from the Web of Science Core Collection database. Visualization and bibliometric analyses of publication quantity and content were conducted to explore temporal trends, spatial distribution, collaborative networks, influential articles, keyword co-occurrence, and research directions.

Results

A total of 177 papers (152 original articles and 25 reviews) were identified from 24 countries/regions, 351 institutions, and 81 journals. Since 2019, the number of studies on this topic has rapidly increased. China and the United States have contributed the highest number of publications and institutions, cultivating the most intimate collaborative relationship. The highest number of articles derive from Sun Yat-sen University, while Frontiers in Oncology has published the highest number of relevant articles. Research on MRI-based AI in this field has mainly focused on preoperative diagnosis and prediction of treatment efficacy and prognosis.

Conclusions

This study provides an objective and comprehensive overview of the publications on MRI-based AI in RC and identifies the present research landscape, hotspots, and prospective trends in this field, which can provide valuable guidance for scholars worldwide.

Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology

Background

Radiomics, which involves the conversion of digital images into high-dimensional data, has been used in oncological studies since 2012. We analyzed the publications that had been conducted on this subject using bibliometric and visual methods to expound the hotpots and future trends regarding radiomics in evaluating lymph node status in oncology.

Methods

Documents published between 2012 and 2023, updated to August 1, 2024, were searched using the Scopus database. VOSviewer, R Package, and Microsoft Excel were used for visualization.

Results

A total of 898 original articles and reviews written in English and be related to radiomics for evaluating lymph node status in oncology, published between 2015 and 2023, were retrieved. A significant increase in the number of publications was observed, with an annual growth rate of 100.77%. The publications predominantly originated from three countries, with China leading in the number of publications and citations. Fudan University was the most contributing affiliation, followed by Sun Yat-sen University and Southern Medical University, all of which were from China. Tian J. from the Chinese Academy of Sciences contributed the most within 5885 authors. In addition, Frontiers in Oncology had the most publications and transcended other journals in recent 4 years. Moreover, the keywords co-occurrence suggested that the interplay of "radiomics" and "lymph node metastasis," as well as "major clinical study" were the predominant topics, furthermore, the focused topics shifted from revealing the diagnosis of cancers to exploring the deep learning-based prediction of lymph node metastasis, suggesting the combination of artificial intelligence research would develop in the future.

Conclusion

The present bibliometric and visual analysis described an approximately continuous trend of increasing publications related to radiomics in evaluating lymph node status in oncology and revealed that it could serve as an efficient tool for personalized diagnosis and treatment guidance in clinical patients, and combined artificial intelligence should be further considered in the future.

Identification of Hub Genes and Immune Infiltration in Coronary Artery Disease: A Risk Prediction Model

Purpose

Our study aimed to establish a prediction model for coronary artery disease (CAD) that integrates immune infiltration and a gene expression signature.

Methods

613 differentially expressed genes (DEGs) and 12 hub genes were screened via the GSE113079 dataset. The pathway enrichment analysis indicated that these genes (613 DEGs and 12 hub genes) were closely associated with the inflammatory and immune responses. Based on the differentially expressed miRNA (DEmiRNA)-DEG regulatory network and immune cell infiltration, the Lasso algorithm constructed a CAD risk prediction model containing the risk score and immune score. Then, ROC-AUC and polymerase chain reaction (PCR) were performed for validation.

Results

Six hub genes (PTGER1, PIK3R1, ADRA2A, CORT, CXCL12, and S1PR5) had a high distinguishing capability (AUC > 0.90). In addition, the miRNAs targeting 12 hub genes were predicted and intersected with the DEmiRNAs, and the DEmiRNA-DEG regulatory network was then constructed. Two LASSO models and a novel CAD risk prediction model were constructed through LASSO regression analysis, and they both accurately obtained the risk of CAD. The CAD risk prediction model shows good performance (AUC = 0.988). We also constructed a valid nomogram, and PCR results verified three downregulation hub genes and one upregulation gene in the CAD risk model.

Conclusion

We demonstrated the molecular mechanism of the hub genes in CAD and provided a valuable tool for predicting the risk of CAD.

Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic performance of Machine Learning (ML) and Deep Learning (DL) models for predicting preoperative Lymph Node Metastasis (LNM) in Colorectal Cancer (CRC) patients.

METHODS

A systematic review and meta-analysis were conducted following PRISMA-DTA and AMSTAR-2 guidelines. We searched PubMed, Web of Science, Embase, and Cochrane Library databases until February 16, 2024. Study quality and risk of bias were assessed using the QUADAS-2 tool. Data were analyzed using STATA v18, applying random-effects models to all analyses.

RESULTS

Twelve studies involving 8321 patients were included, with most published in 2021-2024 (9/12). The pooled AUC of ML models for predicting LNM in CRC patients was 0.87 (95% CI: 0.82-0.91, I2:86.17) with a sensitivity of 78% (95% CI: 69-87%) and a specificity of 77% (95% CI: 64%-90%). In addition, when assessing the AUC reported by radiologists, both junior and senior radiologists had similar performance, significantly lower than the ML models. (P < 0.001). Subgroup analysis revealed higher AUCs in prospective studies (0.95, 95% CI: 0.87-1) compared to retrospective studies (0.85, 95% CI: 0.81-0.89) (P = 0.03). Studies without external validation exhibited significantly higher AUCs than those with external validation (P < 0.01). While there was no significant difference in AUC and sensitivity between the T1-T2 and T2-T4 stages, specificity was significantly higher in the T2-T4 stages than the low stages of T1 and T2 (95%, 95% CI: 92-98% vs. 61%, 95% CI: 44-78%; P < 0.01).

CONCLUSION

ML models demonstrate strong potential for preoperative LNM staging and treatment planning in CRC, potentially reducing the need for additional surgeries and related health and financial burdens. Further prospective multicenter studies, with standardized reporting of algorithms, modality parameters, and LNM staging, are needed to validate these findings.

Improving Prediction Accuracy of Residual Axillary Lymph Node Metastases in Node-Positive Triple-Negative Breast Cancer: A Radiomics Analysis of Ultrasound-Guided Clip Locations Using the SHAP Method

RATIONALE AND OBJECTIVES

To construct a radiomics nomogram derived from multiparametric ultrasound (US) imaging using the SHapley Additive exPlanations (SHAP) method for the accurate identification of residual axillary lymph node metastases post-neoadjuvant chemotherapy (NAC) among patients with triple-negative breast cancer (TNBC).

METHODS

A total of 405 consecutive patients with pathologically confirmed TNBC between 2016 and 2023 were recruited in the study and were divided into training (n = 284) and validation cohorts (n = 121). Radiomics features capturing detailed tumor characteristics were extracted from pre-NAC gray-scale US images at the locations of US-guided clip placement. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy algorithm were employed to identify key features and formulate the radiomics signature (RS). A nomogram based on US radiomics was then constructed using multivariable logistic regression analysis. The predictive efficacy of this model was evaluated through receiver operating characteristic curve analysis, calibration assessment, and decision curve analysis. SHAP summary plots were used to visualize the distribution of SHAP values across all features.

RESULTS

The nomogram integrates clinical and US characteristics with RS, yielded optimal AUC of 0.922 (95% CI, 0.890-0.954) in the training cohort, 0.904 (95% CI, 0.853-0.955) in the validation cohort. The calibration and decision curves confirmed favorable calibration and clinical value of the nomogram. SHAP provided further insight into the contributions of each feature to the model's outcomes.

CONCLUSION

The combined multiparametric US based radiomics nomogram plays a potential role in predicting residual axillary lymph node metastases after NAC in TNBCs.

Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging

This study was performed to investigate the diagnostic value of radiomics models constructed by fat suppressed T2-weighted imaging (T2WI-FS) and contrast-enhanced T1-weighted imaging (CET1) based on magnetic resonance imaging (MRI) for differentiation of osteosarcoma (OS) and chondrosarcoma (CS). In this retrospective cohort study, we included all inpatients with pathologically confirmed OS or CS from Second Xiangya Hospital of Central South University (Hunan, China) as of October 2020. Demographic and imaging variables were extracted from electronic medical records and compared between OS and CS group. Totals of 530 radiomics features were extracted from CET1 and T2WI-FS sequences based on MRI. The least absolute shrinkage and selection operator (LASSO) method was used for screening and dimensionality reduction of the radiomics model. Multivariate logistic regression analysis was performed to construct the radiomics model, and receiver operating characteristic curve (ROC) was generated to evaluate the diagnostic accuracy of the radiomics model. The training cohort and validation cohort included 87 and 29 patients, respectively. 8 CET1 features and 15 T2WI-FS features were screened based on the radiomics features. In the training group, the area under the receiver-operator characteristic curve (AUC) value for CET1 and T2WI-FS sequences in the radiomics model was 0.894 (95% CI 0.817-0.970) and 0.970 (95% CI 0.940-0.999), respectively. In the validation group, the AUC value for CET1 and T2WI-FS sequences in the radiomics model was 0.821 (95% CI 0.642-1.000) and 0.899 (95% CI 0.785-1.000), respectively. In this study, we developed a radiomics model based on T2WI-FS and CET1 sequences to differentiate between OS and CS. This model exhibits good performance and can help clinicians make decisions and optimize the use of healthcare resources.

Clinical Significance of Combined Detection of CCL22 and IL-1 as Potential New Bronchial Inflammatory Mediators in Children's Asthma

BACKGROUNDS

Severe asthma is a significant health burden because children with severe asthma are vulnerable to medication-related side effects, life-threatening deterioration, and impaired quality of life. However, there is a lack of data to elucidate the role of inflammatory variables in asthma. This study aimed to compare the levels of inflammatory factors in serum and sputum in children with acute and stable asthma to those in healthy children and the ability to predict clinical response to azithromycin therapy.

METHODS

This study recruited 95 individuals aged 1-3 years old and collected data from January 2018 to 2020. We examined serum and sputum inflammatory factors and constructed the least absolute shrinkage and selection operator (LASSO) model. Predictive models were constructed through multifactor logistic regression and presented in the form of column-line plots. The performance of the column-line diagrams was measured by subject work characteristics (ROC) curves, calibration plots, and decision curve analysis (DCA). Then, filter-paper samples were collected from 45 children with acute asthma who were randomly assigned to receive either azithromycin (10 mg/kg, n = 22) or placebo (n = 23). Pretreatment levels of immune mediators were then analyzed and compared with clinical response to azithromycin therapy.

RESULTS

Of the 95 eligible participants, 21 (22.11%) were healthy controls, 29 (30.53%) had stable asthma, and 45 (47.37%) had acute asthma. The levels of interferon-γ (IFN-γ), tumor necrosis factor-a (TNF-α), chemokine CCL22 (CCL22), interleukin 12 (IL-12), chemokine CCL4 (CCL4), chemokine CCL2 (CCL2), and chemokine CCL13 (CCL13）were significantly higher in the acute asthma group than in the stable asthma group. A logistic regression analysis was performed using CCL22 and IL-1 as independent variables. Additionally, IFN-γ, TNF-α, IL-1, IL-13, and CCL22 were identified in the LASSO model. Finally, we found that CCL22 and IL-1 were more responsive in predicting the response to azithromycin treatment.

CONCLUSION

Our results show that CCL22 and IL-1 are both representative markers during asthma symptom exacerbations and an immune mediator that can predict response to azithromycin therapy.

Development and validation of a prognostic nomogram in patients aged ≥65 years with stage I-II non-small cell lung cancer treated with stereotactic body radiotherapy

INTRODUCTION

This study aims to discern the efficacy and toxicity of stereotactic body radiotherapy (SBRT) in older adults with stage I-II non-small cell lung cancer (NSCLC) and establish a prognostic nomogram for these patients.

MATERIALS AND METHODS

One hundred forty-two patients (aged ≥65 years) with clinically-confirmed stage I-II NSCLC treated with SBRT from 2009 to 2020 were enrolled in the study. Primary end points included overall survival (OS), progression free survival (PFS), cumulative incidences of local failure (LF), regional failure (RF), distant failure (DF), and toxicity. A nomogram for OS was developed and validated internally using one thousand bootstrap resamplings.

RESULTS

The median times to LF, RF, and DF were 22.1 months, 26.9 months and 24.1 months, respectively. The 1-, 3-, and 5-year PFS rates from the start of SBRT were 79.4 %, 53.1 %, and 38.9 %, respectively. Performance status, pre-SBRT platelet to lymphocyte ratio (PLR), and planning tumor volume (PTV) were predictive of PFS. The 1-, 3-, and 5-year OS rates from the start of SBRT were 90.8 %, 67.9 % and 47.6 %, respectively. In multivariate analysis, good performance status, a low level of pre-SBRT PLR, and small tumor size were associated with better prognosis, all of which were included in the nomogram. The model showed optimal discrimination, with a C-index of 0.651 and good calibration. The most common adverse reactions were grade 1-2, such as anemia, cough, and fatigue.

DISCUSSION

SBRT is a reasonable treatment modality for early-stage NSCLC in older adults. It achieved good survival outcomes and low toxicity. The proposed nomogram may be able to estimate individual outcomes for these patients.

Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model

BACKGROUND

PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs).

PATIENTS AND METHODS

An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally.

RESULTS

Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm).

CONCLUSIONS

Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.

Development and validation of a dynamic nomogram for short-term survival in acute heart failure patients with acute kidney injury upon ICU admission

Objective

The objective of this study is to develop and validate an effective prognostic nomogram for predicting the short-term survival rate of patients with acute heart failure (AHF) complicated by acute kidney injury (AKI) who are admitted to the intensive care unit (ICU).

Patients and methods

We conducted an analysis of data from patients of AHF with AKI spanning the period from 2008 to 2019, utilizing the MIMIC-IV database. Patients were randomly divided into training and validation sets. The training set employed the least absolute shrinkage and selection operator regression model to identify predictors of AKI. Subsequently, a dynamic nomogram was constructed using multivariate Cox regression analysis within the training set and was subsequently validated using the validation set. The nomogram's predictive accuracy, calibration, and clinical utility were evaluated through the concordance index (C-index), calibration plots, and decision curve analysis (DCA).

Results

A total of 978 AHF patients with AKI were analyzed. Multivariate analysis identified serum creatinine, race, age, use of human albumin, use of vasoactive drug, and hemoglobin as independent predictors significantly influencing the short-term prognosis of AHF patients with AKI upon ICU admission. The C-index for the training and validation sets were 0.81 (95%CI: 0.74-0.87) and 0.80 (95 % CI: 0.67-0.92), respectively. The calibration plot of the nomogram demonstrated a close alignment between predicted and observed probabilities. Furthermore, the DCA confirmed the clinical utility of the nomogram.

Conclusions

This study presents a dynamic nomogram that incorporates clinical risk factors and can be conveniently utilized to predict short-term prognosis for AHF patients with AKI upon ICU admission.