Objectives: Stereotactic radiosurgery (SRS), a therapy that uses radiation to treat brain tumors, has become a significant treatment procedure for patients with brain metastasis (BM). However, a proportion of patients have been found to be at risk of local failure (LF) after treatment. Hence, accurately identifying patients with LF risk after SRS treatment is critical to the development of successful treatment plans and the prognoses of patients. To accurately predict BM patients with the occurrence of LF after SRS therapy, we develop and validate a machine learning (ML) model based on pre-treatment multimodal magnetic resonance imaging (MRI) radiomics and clinical risk factors.Patients and methods: In this study, 337 BM patients were included (247, 60, and 30 in the training set, internal validation set, and external validation set, respectively). Four clinical features and 223 radiomics features were selected using least absolute shrinkage and selection operator (LASSO) and Max-Relevance and Min-Redundancy (mRMR) filters. We establish the ML model using the selected features and the support vector machine (SVM) classifier to predict the treatment response of BM patients to SRS therapy.Results: In the training set, the SVM classifier that uses a combination of clinical and radiomics features demonstrates outstanding discriminative performance (AUC=0.95, 95% CI: 0.93-0.97). Moreover, this model also achieves satisfactory results in the validation sets (AUC=0.95 in the internal validation set and AUC=0.93 in the external validation set), demonstrating excellent generalizability.Conclusions: This ML model enables a non-invasive prediction of the treatment response of BM patients receiving SRS therapy, which can in turn assist neurologist and radiation oncologists in the development of more precise and individualized treatment plans for BM patients.

Purpose: The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2–4 based on preoperative conventional multimodal MRI radiomics. Patients and Methods: Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients’ preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA). Results: According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA. Conclusions: The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2–4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management.

Purpose: This study aimed to delineate critical factors, particularly immune-inflammatory biomarkers, that predict therapeutic response and overall survival (OS) in non-small cell lung cancer (NSCLC) patients with brain metastases (BM) undergoing stereotactic radiosurgery (SRS), and to develop novel decision-tree and nomogram models for prognostication. Patients and methods: In this retrospective study, we analyzed data from 464 NSCLC patients with BM treated with SRS between February 2016 and November 2022. The cohort was randomly split into training and validation sets (7:3 ratio). A C5.0 algorithm was employed to build a decision tree model for treatment response. Prognostic factors for OS were identified via univariate and multivariate Cox regression, and subsequently used to construct graphical and online nomograms. Model performance was assessed with calibration curves and the C-index. Results: The median OS for the entire cohort was 15.8 months (95% confidence interval [CI]: 14.6 to 17.0 months). The decision tree model for treatment response identified NLR as a key predictor, alongside volume of brain metastases, Score Index for Radiosurgery (SIR), edema index (EI), and maximum diameter. Multivariate Cox analysis identified age, volume of brain metastases, EI, and SIR as independent prognostic factors for OS. Graphical and dynamic nomograms were developed based on these factors (available at: https://helloshinyweb.shinyapps.io/brain_metastasis_from_NSCLC/ ). The calibration curves demonstrated good consistency between predicted and actual survival, and the C-index indicated a moderate discriminative ability. Conclusions: We identified that immune-inflammatory profiles and radiological-clinical factors are significant predictors for treatment response and OS in NSCLC patients with BM undergoing SRS. The developed decision tree and nomogram models, which incorporate immune-inflammatory profiles, provide user-friendly tools to assist clinicians in optimizing personalized management for this patient population.

Objectives: To identify the critical factors associated with the progression-free survival (PFS) and overall survival (OS) of high-grade glioma (HGG) in adults who have received standard treatment and establish a novel graphical nomogram and an online dynamic nomogram. Patients and Methods: This is a retrospective study of adult HGG patients receiving standard treatment (surgery, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy) at Huashan Hospital, Fudan University between January 2017 and December 2019. We used uni- and multi-variable COX models to identify the significant prognostic factors for PFS and OS. Based on the significant predictors, graphical and online nomograms were established. Results: A total of 246 patients were enrolled in the study based on the inclusion criteria. The average PFS and OS were 22.99 ± 11.43 and 30.51 ± 13.73 months, respectively. According to the multi-variable COX model, age, extent of resection (EOR), and IDH mutation were associated with PFS and OS, while edema index (EI) was relevant to PFS. In addition, patients with IDH and TERT promoter co-mutations had longer PFSs and OSs, and no apparent survival benefit was found in the long-cycle TMZ adjuvant chemotherapy compared with the standard Stupp protocol. Based on these critical factors, a graphical nomogram and online nomogram were developed for predicting PFS and OS, respectively. The calibration curve showed favorable consistency between the predicted and actual survival rates. C-index and time-dependent AUC showed good discrimination abilities. Conclusions: We identified the significant predictors for the PFS and OS of HGG adults receiving standard treatment and established user-friendly nomogram models to assist neurosurgeons in optimizing clinical management and treatment strategies.

Background: Primary central nervous system lymphoma (PCNSL) is a rare extranodal non-Hodgkin’s lymphoma that occurs in the central nervous system. Although sensitive to chemotherapy, 35–60% of PCNSL patients still relapse within 2 years after the initial treatment. High-dose methotrexate (HD-MTX) rechallenge is generally used in recurrent PCNSL, especially for patients who have achieved a response after initial methotrexate (MTX) treatment. However, the overall remission rate (ORR) of HD-MTX rechallenge is about 70–80%. Additionally, the side effects of HD-MTX treatment endanger the health of patients and affect their quality of life. Methods: This is a retrospective study of patients with first relapse PCNSL at Huashan Hospital, Fudan University between January 2000 and November 2020. By comparing the clinical characteristics and radiological manifestations of first relapsed PCNSL patients with remission and non-remission after receiving HD-MTX rechallenge, we screened out the key factors associated with HD-MTX rechallenge treatment response, to provide some help for the selection of salvage treatment strategies for patients with recurrent PCNSL. Additionally, patients with remission after HD-MTX rechallenge were followed up to identify the factors related to progression-free survival of the second time (PFS2) (time from the first relapse to second relapse/last follow-up). The Kruskal–Wallis and Pearson chi-square tests were performed to examine the univariate association. Further, multivariable logistic regression analysis was used to study the simultaneous effect of different variables. Results: A total of 207 patients were enrolled in the study based on the inclusion criteria, including 114 patients in the remission group (RG) and 81 patients in the non-remission group (nRG), and 12 patients were judged as having a stable disease. In Kruskal–Wallis and Pearson chi-square tests, progression-free survival rates for first time (PFS1) and whether the initial treatment was combined with consolidated whole brain radiotherapy (WBRT) were related to the response to HD-MTX rechallenge treatment, which was further validated in regression analysis. Further, after univariate analysis and regression analysis, KPS was related to PFS2. Conclusions: For PCNSL patients in their first relapse, HD-MTX rechallenge may be an effective salvage treatment. PFS1 and whether initial treatment was combined with consolidation WBRT were associated with HD-MTX rechallenge treatment response. In addition, patients with higher KPS at the time of the first relapse had a longer PFS2 after HD-MTX rechallenge treatment.