Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram

Clinical combined PET/CT radiomics model prediction of benefit from platinum-based chemotherapy and chemoradiotherapy in patients with small cell lung cancer

OBJECTIVE

To develop and validate a clinical combined radiomics model for predicting the treatment response and long-term survival prognosis of small cell lung cancer (SCLC) patients receiving platinum-based chemotherapy, as well as survival outcomes following chemoradiotherapy.

METHODS

A total of 98 SCLC patients treated with platinum-based first-line chemotherapy were included in this study. Five prediction models for assessing the short-term efficacy of platinum-based first-line chemotherapy were developed using a logistic regression algorithm. The performance of the models was assessed by calculating the area under the curve of the receiver operating characteristic curves. For predicting progression-free survival (PFS) and overall survival in the platinum-based chemotherapy group and the chemoradiotherapy group, the optimal cutoff value was determined using X-tile software. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare survival outcomes.

RESULTS

Among the five models for predicting short-term efficacy, the clinical combined positron emission tomography/computed tomography (PET/CT) radiomics model performed the best, achieving areas under the curve of 0.832 and 0.833 for the training and test sets, respectively. The Kaplan-Meier survival analysis indicated that both the high-scoring Combine group and high-scoring PET/CT group were significantly associated with worse PFS and worse overall survival in the platinum-only chemotherapy group. Additionally, the high-scoring CT group was significantly associated with worse PFS in the chemoradiotherapy group.

CONCLUSION

The clinical combined PET/CT radiomics model can noninvasively and accurately predict the response to platinum-based treatments in SCLC as well as long-term survival prognosis, which can contribute to personalized treatment strategies and guide precision therapy for SCLC patients.

Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era

Introduction: Chronic osteomyelitis is a complex clinical condition that is associated with a high recurrence rate. Traditional surgical interventions often face challenges in achieving a balance between thorough debridement and managing resultant bone defects. Radiomics is an emerging technique that extracts quantitative features from medical images to reveal pathological information imperceptible to the naked eye. This study aims to investigate the potential of radiomics in optimizing osteomyelitis diagnosis and surgical treatment. Methods: Magnetic resonance imaging (MRI) scans of 93 suspected osteomyelitis patients were analyzed. Radiomics features were extracted from the original lesion region of interest (ROI) and an expanded ROI delineated by enlarging the original by 5 mm. Feature selection was performed and support vector machine (SVM) models were developed using the two ROI datasets. To assess the diagnostic efficacy of the established models, we conducted receiver operating characteristic (ROC) curve analysis, employing histopathological results as the reference standard. The model's performance was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy. Discrepancies in the ROC between the two models were evaluated using the DeLong method. All statistical analyses were carried out using Python, and a significance threshold of p < 0.05 was employed to determine statistical significance. Results and Discussion: A total of 1,037 radiomics features were extracted from each ROI. The expanded ROI model achieved significantly higher accuracy (0.894 vs. 0.821), sensitivity (0.947 vs. 0.857), specificity (0.842 vs. 0.785) and AUC (0.920 vs. 0.859) than the original ROI model. Key discriminative features included shape metrics and wavelet-filtered texture features. Radiomics analysis of MRI exhibits promising clinical translational potential in enhancing the diagnosis of chronic osteomyelitis by accurately delineating lesions and identifying surgical margins. The inclusion of an expanded ROI that encompasses perilesional tissue significantly improves diagnostic performance compared to solely focusing on the lesions. This study provides clinicians with a more precise and effective tool for diagnosis and surgical decision-making, ultimately leading to improved outcomes in this patient population.

Prognosis value of circulating tumor cell PD‑L1 and baseline characteristics in patients with NSCLC treated with immune checkpoint inhibitors plus platinum‑containing drugs

Immune checkpoint inhibitors (ICIs) combined with platinum-containing chemotherapy are recommended as the standard first-line treatment for non-small cell lung cancer (NSCLC). However, specific prognostic markers for this combination therapy are yet to be identified. Evaluation of circulating tumor cells (CTCs) and cell surface programmed death-ligand 1 (PD-L1) exhibits potential in predicting the efficacy of the aforementioned combination therapy. Thus, the present study aimed to evaluate the prognostic value of CTC PD-L1 testing and other clinical characteristics in patients with NSCLC treated with combination therapy as first-line treatment. In total, 40 patients with advanced NSCLC were included in the present study, and all patients underwent CTC PD-L1 testing at initial diagnosis to determine the association between CTC PD-L1 and tissue PD-L1. The prognostic value of CTC PD-L1 and the baseline characteristics of 26 patients with NSCLC were analyzed, and the prognostic values of changes in CTC PD-L1 and baseline characteristics during 6 months of treatment were further explored. Results of the present study demonstrated that there was no association between CTC PD-L1 and tissue PD-L1 levels. After 6 months of combination therapy, tumor shrinkage, CYFA19 levels and treatment maintenance were associated with progression-free survival (PFS) of patients. Notably, CTC PD-L1 and tissue PD-L1 levels, TNM stage, nutritional score, inflammation score and other blood indicators were not associated with PFS. In conclusion, the evaluation of CTCs and CTC PD-L1 suggested that undetectable CTCs at 6 months of NSCLC treatment are associated with a good prognosis. In addition, negative CTC PD-L1 expression may change to positive CTC PD-L1 expression in line with disease progression, and this may be indicative of poor prognosis.

Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion

Objective

To explore the value of the magnetic resonance imaging (MRI) radiomics model in predicting prostate cancer (PCa) invasion.

Methods

Clinical data of 86 pathologically confirmed PCa patients in our hospital were collected, including 44 cases in the invasive group and 42 cases in the non-invasive group. All patients underwent MRI examinations, and the same parameters were used. The lesion area was manually delineated and the radiomics features were extracted from T2WI. The radiomics signature based on LASSO regression was established. Besides, logistic regression was used to identify independent clinical predictors, and a combined model incorporating the radiomics signature and independent clinical risk factor was constructed. Finally, the receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) was performed to compare the prediction efficiency and clinical benefit of each model.

Results

A total of 867 radiomics features were obtained, and six of them were incorporated into the radiomics model. Multivariate logistic regression analysis exhibited the Gleason score as an independent clinical risk factor for PCa invasion. ROC results showed that the performance of the radiomics model was comparable to that of the clinical-radiomics model in predicting PCa invasion, and it was better than that of the single Gleason score. DCA also confirmed the considerable clinical application value of the radiomics and the clinical-radiomics models.

Conclusion

As a simple, non-invasive, and efficient method, the radiomics model has important predictive value for PCa invasion.