Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma

Predicting vertebral compression fracture prior to spinal SBRT using radiomics from planning CT

PURPOSE

The purpose of the study was to develop a predictive model for vertebral compression fracture (VCF) prior to spinal stereotactic body radiation therapy (SBRT) using radiomics features extracted from pre-treatment planning CT images.

METHODS

A retrospective analysis was conducted on 85 patients (114 spinal lesions) who underwent spinal SBRT. Radiomics features were extracted from pre-treatment planning CT images and used to develop a predictive model using a classification algorithm selected from nine different machine learning algorithms. Four different models were trained, including clinical features only, clinical and radiomics features, radiomics and dosimetric features, and all features. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC).

RESULTS

The model that used all features (radiomics, clinical, and dosimetric) showed the highest performance with an AUC of 0.871. The radiomics and dosimetric features model had the superior performance in terms of accuracy, precision, recall, and F1-score.

CONCLUSION

The developed predictive model based on radiomics features extracted from pre-treatment planning CT images can accurately predict the likelihood of VCF prior to spinal SBRT. This model has significant implications for treatment planning and preventive measures for patients undergoing spinal SBRT. Future research can focus on improving model performance by incorporating new data and external validation using independent data sets.

Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC

The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79-0.81) and 0.86 (0.87-0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy

OBJECTIVE

We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy.

METHODS

Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.

RESULTS

The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram.

CONCLUSION

ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.

Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images

PURPOSE

To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features.

METHOD AND MATERIALS

This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves.

RESULTS

The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort.

CONCLUSION

Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

Hypoxia- and Inflammation-Related Transcription Factor SP3 May Be Involved in Platelet Activation and Inflammation in Intracranial Hemorrhage

The purpose of this study was to identify the biomarkers implicated in the development of intracranial hemorrhage (ICH) and potential regulatory pathways. In the transcriptomic data for patients with ICH, we identified DEmiRNAs and DEmRNAs related to hypoxia, inflammation, and their transcription factors (TFs). An ICH-based miRNA-TF-mRNA regulatory network was thus constructed, and four biomarkers (TIMP1, PLAUR, DDIT3, and CD40) were screened for their association with inflammation or hypoxia by machine learning. Following this, SP3 was found to be a transcription factor involved in hypoxia and inflammation, which regulates TIMP1 and PLAUR. From the constructed miRNA-TF-mRNA regulatory network, we identified three axes, hsa-miR-940/RUNX1/TIMP1, hsa-miR-571/SP3/TIMP1, and hsa-miR-571/SP3/PLAUR, which may be involved in the development of ICH. Upregulated TIMP1 and PLAUR were validated in an independent clinical cohort 3 days after ICH onset. According to Gene Set Enrichment Analysis (GSEA), SP3 was discovered to be important in interleukin signaling and platelet activation for hemostasis. Transcription factor SP3 associated with hypoxia or inflammation plays an important role in development of ICH. This study provides potential targets for monitoring the severity of inflammation and hypoxia in patients with ICH.