Non-invasive preoperative imaging differential diagnosis of pineal region tumor: A novel developed and validated multiparametric MRI-based clinicoradiomic model

A comprehensive dataset of germinoma on MRI/CT with clinical and radiomic data

Intracranial germ cell tumors (GCTs) are rare neoplasms with a peak incidence in adolescents. Germinoma is the most common histological subtype of intracranial GCTs. Its symptoms include intracranial hypertension, visual field defects, and hormonal disorders, affecting the physical health of adolescents. Germinoma is sensitive to chemo-radiotherapy, and most patients do not require neurosurgical resection. Therefore, improving the accuracy of germinoma diagnosis helps to avoid unnecessary surgery. At present, the application of artificial intelligence (AI) in medical imaging has improved the accuracy of disease diagnosis. However, few studies focused on the AI model to diagnosis germinoma and there are no publicly available imaging datasets for germinoma. This study aimed to create a comprehensive dataset for germinoma using magnetic resonance imaging/computed tomography findings with clinical and radiomic data to train and validate AI models. Featuring 65 pathologically confirmed germinomas, the dataset included axial T2-weighted imaging, T2-weighted fluid-attenuated inversion recovery, T1-weighted imaging, T1-weighted imaging with contrast enhancement, diffusion-weighted MR imaging, CT images, clinical data, and morphological and radiomic-based features obtained by segmentation.

Noninvasive Radiomics Approach Predicts Dopamine Agonists Treatment Response in Patients with Prolactinoma: A Multicenter Study

RATIONALE AND OBJECTIVES

The first-line treatment for prolactinoma is drug therapy with dopamine agonists (DAs). However, some patients with resistance to DA treatment should prioritize surgical treatment. Therefore, it is crucial to accurately identify the drug treatment response of prolactinoma before treatment. The present study was performed to determine the DA treatment response of prolactinoma using a clinical radiomic model that incorporated radiomic and clinical features before treatment.

MATERIALS AND METHODS

In total, 255 patients diagnosed with prolactinoma were retrospectively divided to training and validation sets. An elastic net algorithm was used to screen the radiomic features, and a fusion radiomic model was established. A clinical radiomic model was then constructed to integrate the fusion radiomic model and the most important clinical features through multivariate logistic regression analysis for individual prediction. The calibration, discrimination, and clinical applicability of the established models were evaluated. 60 patients with prolactinoma from other centers were used to validate the performance of the constructed model.

RESULTS

The fusion radiomic model was constructed from three significant radiomic features, and the area under the curve in the training set and validation set was 0.930 and 0.910, respectively. The clinical radiomic model was constructed using the radiomic model and three clinical features. The model exhibited good recognition and calibration abilities as evidenced by its area under the curve of 0.96, 0.92, and 0.92 in the training, validation, and external multicenter validation set, respectively. Analysis of the decision curve showed that the fusion radiomic model and clinical radiomic model had good clinical application value for DA treatment response prediction in patients with prolactinoma.

CONCLUSION

Our clinical radiomic model demonstrated high sensitivity and excellent performance in predicting DA treatment response in prolactinoma. This model holds promise for the noninvasive development of individualized diagnosis and treatment strategies for patients with prolactinoma.

Magnetic resonance imaging (MRI) radiomics in paediatric neuro-oncology: A systematic review of clinical applications, feature interpretation, and biological insights in the characterisation and management of childhood brain tumours

Background

Childhood brain tumours, even though rare, present with significant diagnostic and treatment challenges. Radiomics involves feature extraction that is data-driven from standard imaging modalities, such as magnetic resonance imaging (MRI). In paediatric brain tumour imaging, MRI is often preferred because it is non-invasive and avoids exposure to radiation, making it safer for children. Radiomic features reveal additional information about tumour morphology and heterogeneity. By integrating biological meaning into imaging data, this approach enhances our understanding of tumour biology, thus supporting improved classification, treatment planning and management.

Purpose

This review focuses on MRI-based radiomics for the diagnosis and prognosis of childhood brain tumours. It assesses various approaches used in image pre-processing, tumour segmentation, feature extraction, and predictive model development to understand their accuracy and outcome, while it aims to understand the biological meaning and interpretation of radiomic features.

Methods

A systematic review was conducted, including 559 MRI-based radiomics studies in PubMed and Engineering Village Compendex databases, following preferred reporting items for systematic reviews and meta-analyses guidelines (PROSPERO registration: CRD42024503524). Data extraction focused on age, sample size, tumour type, pre-processing techniques, segmentation methods, feature extraction, and performance metrics.

Results

Nineteen studies were included, primarily addressing ependymoma (EP) and medulloblastoma (MB). Common pre-processing methods included intensity normalisation (n = 11) and bias correction (n = 4). GLCM (n = 12) and GLRLM (n = 7) were frequently used features, with LASSO (n = 8) and PCA (n = 2) as leading selection methods. SVM was the most used classification algorithm (n = 9), with an AUC range of 0.858-0.977. We have included the biological meaning and clinical significance of radiomic features to further understand why these insights are important.

Conclusion

While challenges such as limited datasets and varied imaging protocols exist, we recommend identifying and integrating the most informative radiomic features to enhance diagnostic and prognostic accuracy in childhood brain tumours, ultimately improving patient outcomes.

The novel developed and validated multiparametric MRI-based fusion radiomic and clinicoradiomic models predict the postoperative progression of primary skull base chordoma

Local progression of primary skull base chordoma (PSBC) is a sign of treatment failure. Predicting the postoperative progression of PSBC can aid in the development of individualized treatment plans to improve patients' progression-free survival (PFS) after surgery. This study aimed to develop a multiparametric MRI-based fusion radiomic model (FRM) and clinicoradiomic model (CRM) via radiomic and clinical analysis and to explore their validity in predicting postoperative progression in PSBC patients before surgery. In this retrospective study, a total of 129 patients with PSBC from our institution, including 57 patients with progression, were enrolled and randomized to the training set (TS) or the validation set (VS) at a 2:1 ratio. Radiomic features were extracted and dimensionally reduced from 3.0 T/axial T2-weighted imaging (T2WI), T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences for each patient, and the features were used for radiomic analysis. Univariate and multivariate Cox regression analyses were used to screen for key clinical factors. We constructed models on the basis of multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate the performance of the clinical model (CM), FRM and CRM. Through analysis, we found that blood supply was the only significantly different clinical factor in the CM. For the FRM, the area under the receiver operating characteristic curve (AUC) of the TS was 0.925, and the calibration curves were consistent across the TS. In the CRM, the AUC of the TS was 0.929, the calibration curve analysis was consistent for both the TS and the VS, and the DCA showed that the net benefit was greater at a threshold probability of > 0% for both the TS and the VS. Our proposed FRM can help clinicians better predict PSBC progression preoperatively, and the use of the CRM can lead to the development of more appropriate protocols to improve patients' PFS after surgery.

Multimodal MRI-based radiomic nomogram for predicting telomerase reverse transcriptase promoter mutation in IDH-wildtype histological lower-grade gliomas

The presence of TERTp mutation in isocitrate dehydrogenase-wildtype (IDHwt) histologically lower-grade glioma (LGA) has been linked to a poor prognosis. In this study, we aimed to develop and validate a radiomic nomogram based on multimodal MRI for predicting TERTp mutations in IDHwt LGA. One hundred and nine IDH wildtype glioma patients (TERTp-mutant, 78; TERTp-wildtype, 31) with clinical, radiomic, and molecular information were collected and randomly divided into training and validation set. Clinical model, fusion radiomic model, and combined radiomic nomogram were constructed for the discrimination. Radiomic features were screened with 3 algorithms (Wilcoxon rank sum test, elastic net, and the recursive feature elimination) and the clinical characteristics of combined radiomic nomogram were screened by the Akaike information criterion. Finally, receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis were utilized to assess these models. Fusion radiomic model with 4 radiomic features achieved an area under the curve value of 0.876 and 0.845 in the training and validation set. And, the combined radiomic nomogram achieved area under the curve value of 0.897 (training set) and 0.882 (validation set). Above that, calibration curve and Hosmer-Lemeshow test showed that the radiomic model and combined radiomic nomogram had good agreement between observations and predictions in the training set and the validation set. Finally, the decision curve analysis revealed that the 2 models had good clinical usefulness for the prediction of TERTp mutation status in IDHwt LGA. The combined radiomics nomogram performed great performance and high sensitivity in prediction of TERTp mutation status in IDHwt LGA, and has good clinical application.

Update on the Diagnosis and Management of Meningiomas

This series of five articles (one original article and four reviews) focuses on the most recent and interesting research studies on the biomolecular and radiological diagnosis and the surgical and medical management of meningiomas [...].

A comprehensive machine-learning model applied to MRI to classify germinomas of the pineal region

BACKGROUND

Pineal region tumors (PRTs) are highly histologically heterogeneous. Germinoma is the most common PRT and is treatable with radiotherapy and chemotherapy. A non-invasive system that helps identify germinoma in the pineal region could reduce lab exams and traumatic therapies.

METHODS

In this retrospective study, 122 patients with histologically confirmed PRTs and pre-operative multi-modal MR images were included. Radiomics features were extracted from different ROIs and image sequences separately. A computational framework that combines a few classification and feature selection algorithms were used to predict histology with radiomics features and demographics. We systemically benchmarked performance of models with feature matrices from all possible combinations of ROIs and image sequences. The Area under the ROC Curve (AUC) was then used to evaluate model performance.

RESULTS

Models with demographics and radiomics features outperform radiomics-only or demographics-only models. The best demographical-radiomics model reached the highest AUC of 0.88 (CI95%: 0.81-0.96). Through the comprehensive evaluation of possible sequence combinations in the differential diagnosis of pineal tumor, T1 and T2 emerged as the most informative sequences for the task. There is imbalanced usage of feature classes as we analyze their proportion in all models.

CONCLUSIONS

The demographical-radiomics model can accurately and efficiently identify germinomas in the pineal region. The preference for MRI sequences, radiomics feature classes, features selection and classification algorithms provide a valuable reference for future attempts at developing classifiers on medical images.

A 2-step prediction model for diagnosis of germinomas in the pineal region

Background

Germinomas are sensitive to radiation and chemotherapy, and their management distinctly differs from other kinds of pineal region tumors. The aim of this study was to construct a prediction model based on clinical features and preoperative magnetic resonance (MR) manifestations to achieve noninvasive diagnosis of germinomas in pineal region.

Methods

A total of 126 patients with pineal region tumors were enrolled, including 36 germinomas, 53 nongerminomatous germ cell tumors (NGGCTs), and 37 pineal parenchymal tumors (PPTs). They were divided into a training cohort (n = 90) and a validation cohort (n = 36). Features were extracted from clinical records and conventional MR images. Multivariate analysis was performed to screen for independent predictors to differentiate germ cell tumors (GCTs) and PPTs, germinomas, and NGGCTs, respectively. From this, a 2-step nomogram model was established, with model 1 for discriminating GCTs from PPTs and model 2 for identifying germinomas in GCTs. The model was tested in a validation cohort.

Results

Both model 1 and model 2 yielded good predictive efficacy, with c-indexes of 0.967 and 0.896 for the diagnosis of GCT and germinoma, respectively. Calibration curve, decision curve, and clinical impact curve analysis further confirmed their predictive accuracy and clinical usefulness. The validation cohort achieved areas under the receiver operating curves of 0.885 and 0.926, respectively.

Conclusions

The 2-step model in this study can noninvasively differentiate GCTs from PPTs and further identify germinomas, thus holding potential to facilitate treatment decision-making for pineal region tumors.

Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning

Background

Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia.

Methods

This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization.

Results

The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors.

Conclusions

This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively.