A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas

Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas

Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas' high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging's role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.

Noninvasive prediction of IDH mutation status in gliomas using preoperative multiparametric MRI radiomics nomogram: A mutlicenter study

PURPOSE

To establish and validate a radiomics nomogram for preoperative prediction of isocitrate dehydrogenase (IDH) mutation status of gliomas in a multicenter setting.

METHODS

414 gliomas patients were collected (306 from local institution and 108 from TCGA). 851 radiomics features were extracted from contrast-enhanced T1-weighted (CE-T1W) and fluid attenuated inversion recovery (FLAIR) sequence, respectively. The features were refined using least absolute shrinkage and selection operator (LASSO) regression combing 10-fold cross-validation. The optimal radiomics features with age and sex were processed by multivariate logistic regression analysis to construct a prediction model, which was developed in the training dataset and assessed in the test and validation dataset. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis were applied in the test and external validation datasets to evaluate the performance of the prediction model.

RESULTS

Ten robust radiomics features were selected from the 1702 features (four CE-T1W features and six FLAIR features). A nomogram was plotted to represent the prediction model. The accuracy and AUC of the radiomics nomogram achieved 86.96% and 0.891(0.809-0.947) in the test dataset and 84.26% and 0.881(0.805-0.936) in the external validation dataset (all p < 0.05). The positive predictive value (PPV) and negative predictive value (NPV) were 83.72% and 87.75% in the test dataset and 87.81% and 82.09% in the external validation dataset.

CONCLUSION

IDH genotypes of gliomas can be identified by preoperative multiparametric MRI radiomics nomogram and might be clinically meaningful for treatment strategy and prognosis stratification of gliomas.

Radiogenomics: a key component of precision cancer medicine

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma

Objectives

In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors.

Methods

In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values.

Results

Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results.

Conclusion

In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

OBJECTIVE

To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma.

DATA AND METHODS

The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness.

RESULTS

Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values.

CONCLUSION

A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.

Predictive value of TCM clinical index for diabetic peripheral neuropathy among the type 2 diabetes mellitus population: A new observation and insight

Aims

The objectives of this study were to identify clinical predictors of the Traditional Chinese medicine (TCM) clinical index for diabetic peripheral neuropathy (DPN) in type 2 diabetes mellitus (T2DM) patients, develop a clinical prediction model, and construct a nomogram.

Methods

We collected the TCM clinical index from 3590 T2DM recruited at the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine from January 2019 to October 2020. The participants were randomly assigned to either the training group (n = 3297) or the validation group (n = 1426). TCM symptoms and tongue characteristics were used to assess the risk of developing DPN in T2DM patients. Through 5-fold cross-validation in the training group, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection. In addition, using multifactor logistic regression analysis, a predictive model and nomogram were developed.

Results

A total of eight independent predictors were found to be associated with the DPN in multivariate logistic regression analyses: advanced age of grading (odds ratio/OR 1.575), smoke (OR 2.815), insomnia (OR 0.557), sweating (OR 0.535), loose teeth (OR 1.713), dry skin (OR 1.831), purple tongue (OR 2.278). And dark red tongue (OR 0.139). The model was constructed using these eight predictor's medium discriminative capabilities. The area under the curve (AUC) of the training set is 0.727, and the AUC of the validation set is 0.744 on the ROC curve. The calibration plot revealed that the model's goodness-of-fit is satisfactory.

Conclusions

We established a TCM prediction model for DPN in patients with T2DM based on the TCM clinical index.

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

BACKGROUND

Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers.

METHODS

A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models.

RESULTS

The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I-II or WHO III-IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively.

CONCLUSIONS

This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.

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.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study

Background

To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas.

Methods

Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC₁₅₀₀, α₁₅₀₀ fitted by 15 b-values (0-1,500 sec/mm²)and DDC₅₀₀₀ and α₅₀₀₀ fitted by 22 b-values (0-5,000 sec/mm²) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters.

Results

MDWI-derived α₁₅₀₀ negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC₁₅₀₀ and DDC₅₀₀₀ negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α₁₅₀₀(r=-0.485; P=0.005) and α₅₀₀₀(r=-0.395; P=0.025).

Conclusions

SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.