A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from glioblastomas

Tumor oxygenation imaging biomarkers using dynamic susceptibility contrast imaging for prediction of IDH mutation status in adult-type diffuse gliomas

OBJECTIVES

To evaluate the role of tumor oxygenation imaging parameters in predicting the isocitrate dehydrogenase (IDH) mutation status in adult-type diffuse gliomas.

METHODS

This retrospective study included 296 patients with adult-type diffuse glioma (237 IDH-wildtype, 59 IDH-mutant). The normalized cerebral blood volume (nCBV), cerebral metabolic rate of oxygen values (CMRO2), capillary transit time heterogeneity, and oxygen extraction fraction (OEF) values from dynamic susceptibility contrast (DSC) imaging and ADC values from DWI were obtained from autosegmented tumor masks. Logistic analyses were performed in entire patients and in a subgroup of 46 patients (15.6%) without CE tumors.

RESULTS

In entire patients, 10th percentile of CMRO2 (odds ratio [OR] = 0.64, p < 0.001) and 10th percentile of OEF (OR = 0.32, p < 0.001) independently predicted IDH mutation, along with age, frontal location, presence of CE tumor, and 90th percentile of nCBV, with an area under the curve (AUC) of 0.92 (95% confidence interval [CI] 0.88-0.95). In the subgroup of patients without CE tumors, 10th percentile of CMRO2 (OR = 0.58, p = 0.044) was an independent predictor for IDH mutation, along with age, and 10th percentile of ADC with an AUC of 0.94 (95% CI 0.83-0.99).

CONCLUSION

Tumor oxygenation parameters, including CMRO2 and OEF, may predict IDH mutation independently of previously known clinical and quantitative imaging data. Lower 10th percentile of CMRO2 and OEF predicts IDH mutation in entire tumors, while lower 10th percentile of CMRO2 predicts IDH mutation in patients without CE tumors.

KEY POINTS

Question The role of tumor oxygenation imaging parameters for predicting the IDH mutation status in adult-type diffuse gliomas is unknown. Findings Lower cerebral metabolic rate of oxygen values (CMRO2) and oxygen extraction fraction (OEF) predicted IDH mutation in entire tumors, while lower CMRO2 predicted IDH mutation in patients without enhancing tumors. Clinical relevance Tumor oxygenation parameters derived from dynamic susceptibility contrast (DSC) perfusion imaging may assist noninvasive prediction of IDH mutation in adult-type diffuse gliomas.

Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas

Molecular subtyping and grading of adult-type diffuse gliomas are essential for treatment decisions and patient prognosis. We introduce GlioMT, an interpretable multimodal transformer that integrates imaging and clinical data to predict the molecular subtype and grade of adult-type diffuse gliomas according to the 2021 WHO classification. GlioMT is trained on multiparametric MRI data from an institutional set of 1053 patients with adult-type diffuse gliomas to predict the IDH mutation status, 1p/19q codeletion status, and tumor grade. External validation on the TCGA (200 patients) and UCSF (477 patients) shows that GlioMT outperforms conventional CNNs and visual transformers, achieving AUCs of 0.915 (TCGA) and 0.981 (UCSF) for IDH mutation, 0.854 (TCGA) and 0.806 (UCSF) for 1p/19q codeletion, and 0.862 (TCGA) and 0.960 (UCSF) for grade prediction. GlioMT enhances the reliability of clinical decision-making by offering interpretability through attention maps and contributions of imaging and clinical data.

MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival

We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene expression and clinicopathological data, was downloaded from online database. Kaplan-Meier survival curves, univariate and multivariate COX analyses, and time-dependent receiver operating characteristic were used to assess the prognostic value of CD44. Following the screening of radiomic features using repeat least absolute shrinkage and selection operator, two radiomics models were constructed utilizing logistic regression and support vector machine for validation purposes. The results indicated that CD44 protein levels were higher in HGG compared to normal brain tissues, and CD44 expression emerged as an independent biomarker of diminished overall survival (OS) in patients with HGG. Moreover, two predictive models based on seven radiomic features were built to predict CD44 expression levels in HGG, achieving areas under the curves (AUC) of 0.809 and 0.806, respectively. Calibration and decision curve analysis validated the fitness of the models. Notably, patients with high radiomic scores presented worse OS (p < 0.001). In summary, our results indicated that the radiomics models effectively differentiate CD44 expression level and OS in patients with HGG.

Leptomeningeal metastases at recurrence in IDH-wildtype glioblastomas: incidence, risk factors, and prognosis based on postcontrast FLAIR imaging

OBJECTIVES

To comprehensively investigate the incidence, risk factors, and prognosis of leptomeningeal metastases (LM) diagnosed at recurrence in IDH-wildtype glioblastoma patients.

MATERIALS AND METHODS

A total of 734 IDH-wildtype glioblastoma patients were enrolled between 2005 and 2022. LM at recurrence was diagnosed with MRI including postcontrast FLAIR. Logistic analysis for development of LM at recurrence was performed with clinical, molecular, imaging (including tumor volume and distance to subventricular zone via automatic segmentation), and surgical data including extent of resection and ventricular entry. The overall survival (OS) was compared between patients with and without LM at recurrence.

RESULTS

The incidence of LM at recurrence based on postcontrast FLAIR was 10.8% (79 patients). On multivariable analysis, younger age at diagnosis (odds ratio (OR) = 0.98, p = 0.011) and ventricular entry (OR = 3.15, p < 0.001) were independent predictors of LM at recurrence. However, patients with LM at recurrence showed no significant difference in OS from patients without LM (log-rank test; p = 0.461), with median OS of 18.0 (95% confidence interval (CI) 16.2-19.8) and 18.5 (95% CI 16.4-20.7) months in patients with and without LM at recurrence, respectively.

CONCLUSION

The incidence of LM at recurrence is relatively high in IDH-wildtype glioblastoma patients. Younger age and ventricular entry during surgery warrant imaging surveillance for LM at recurrence. As LM at recurrence showed no significant OS compromise and larger extent of resection (EOR) is associated with survival benefits, ventricular entry during maximal safe resection may be acceptable.

KEY POINTS

Question The incidence, risk factors, and prognosis of leptomeningeal metastases (LM) diagnosed at recurrence in IDH-wildtype glioblastoma patients are currently unknown. Findings LM at recurrence occurred in 10.8% of cases, with younger age and ventricular entry as risk factors, but no significant difference in survival outcomes between groups. Clinical relevance The incidence, risk factors, and prognosis of LM at recurrence were investigated in IDH-wildtype glioblastoma patients with postcontrast FLAIR. Younger age and ventricular entry warrant surveillance of LM at recurrence, while the overall survival is not as discouraging as expected.

Leptomeningeal metastases in isocitrate dehydrogenase-wildtype glioblastomas revisited: Comprehensive analysis of incidence, risk factors, and prognosis based on post-contrast fluid-attenuated inversion recovery

BACKGROUND

The incidence of leptomeningeal metastases (LM) has been reported diversely. This study aimed to investigate the incidence, risk factors, and prognosis of LM in patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma.

METHODS

A total of 828 patients with IDH-wildtype glioblastoma were enrolled between 2005 and 2022. Baseline preoperative MRI including post-contrast fluid-attenuated inversion recovery (FLAIR) was used for LM diagnosis. Qualitative and quantitative features, including distance between tumor and subventricular zone (SVZ) and tumor volume by automatic segmentation of the lateral ventricles and tumor, were assessed. Logistic analysis of LM development was performed using clinical, molecular, and imaging data. Survival analysis was performed.

RESULTS

The incidence of LM was 11.4%. MGMTp unmethylation (odds ratio [OR] = 1.92, P = .014), shorter distance between tumor and SVZ (OR = 0.94, P = .010), and larger contrast-enhancing tumor volume (OR = 1.02, P < .001) were significantly associated with LM. The overall survival (OS) was significantly shorter in patients with LM than in those without (log-rank test; P < .001), with median OS of 12.2 and 18.5 months, respectively. The presence of LM remained an independent prognostic factor for OS in IDH-wildtype glioblastoma (hazard ratio = 1.42, P = .011), along with other clinical, molecular, imaging, and surgical prognostic factors.

CONCLUSIONS

The incidence of LM is high in patients with IDH-wildtype glioblastoma, and aggressive molecular and imaging factors are correlated with LM development. The prognostic significance of LM based on post-contrast FLAIR imaging suggests the acknowledgment of post-contrast FLAIR as a reliable diagnostic tool for clinicians.

Identification of prognostic imaging biomarkers in H3 K27-altered diffuse midline gliomas in adults: impact of tumor oxygenation imaging biomarkers on survival

PURPOSE

To investigate prognostic markers for H3 K27-altered diffuse midline gliomas (DMGs) in adults with clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics.

METHODS

Retrospective chart and imaging reviews were conducted on 32 adults with H3 K27-altered DMGs between 2017 and 2023. Clinical and qualitative imaging characteristics were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate normalized cerebral blood volume (nCBV), capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen (rCMRO2), and mean ADC values. Leptomeningeal metastases (LM) was diagnosed with imaging. Cox analyses were conducted to determine predictors of overall survival (OS) in entire patients and a subgroup of patients with contrast-enhancing (CE) tumor.

RESULTS

The median patient age was 40.5 years (range 19.9-75.7), with an OS of 30.3 months (interquartile range 11.3-32.3). In entire patients, the presence of LM was the only independent predictor of OS (hazard ratio [HR] = 6.01, P = 0.009). In the subgroup of 23 (71.9%) patients with CE tumors, rCMRO2 of CE tumor (HR = 1.08, P = 0.019) and the presence of LM (HR = 5.92, P = 0.043) were independent predictors of OS.

CONCLUSION

The presence of LM was independently associated with poor prognosis in adult patients with H3 K27-altered DMG. In patients with CE tumors, higher rCMRO2 of CE tumor, which may reflect higher metabolic activity in the tumor oxygenation microenvironment, may be a useful imaging biomarker to predict poor prognosis.

Revisiting oligodendroglioma grading in the 2021 WHO classification: calcification and larger contrast-enhancing tumor volume may predict higher oligodendroglioma grade

PURPOSE

To investigate whether qualitative and quantitative imaging phenotypes can predict the grade of oligodendroglioma.

METHODS

Retrospective chart and imaging reviews were conducted on 180 adults with oligodendroglioma (IDH-mutant and 1p/19q codeleted) between 2005 and 2021. Qualitative imaging characteristics including tumor location, calcification, gliomatosis cerebri, cystic change, necrosis, and infiltrative pattern were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate total, contrast-enhancing (CE), non-enhancing (NE), and necrotic tumor volumes. Logistic analyses were conducted to determine predictors of oligodendroglioma grade.

RESULTS

This study included 180 patients (84 [46.7%] with grade 2 and 96 [53.3%] with grade 3 oligodendrogliomas), with a median age of 42 years (range 23-76 years), comprising 91 females and 89 males. On univariable analysis, calcification (odds ratio [OR] = 6.00, P < 0.001), necrosis (OR = 21.84, P = 0.003), presence of CE tumor (OR = 7.86, P < 0.001), larger total (OR = 1.01, P < 0.001), larger CE (OR = 2.22, P = 0.010), and larger NE (OR = 1.01, P < 0.001) tumor volumes were predictors of grade 3 oligodendroglioma. On multivariable analysis, calcification (OR = 3.79, P < 0.001) and larger CE tumor volume (OR = 2.70, P = 0.043) remained as independent predictors of grade 3 oligodendroglioma. The multivariable model exhibited an AUC, accuracy, sensitivity, specificity of 0.78 (95% confidence interval 0.72-0.84), 72.8%, 79.2%, 69.1%, respectively.

CONCLUSION

Presence of calcification and larger CE tumor volume may serve as useful imaging biomarkers for prediction of oligodendroglioma grade.

CLINICAL RELEVANCE STATEMENT

Assessment of intratumoral calcification and CE tumor volume may facilitate accurate preoperative estimation of oligodendroglioma grade. Presence of intratumoral calcification and larger contrast-enhancing tumor volume were the significant predictors of higher grade oligodendroglioma based on the 2021 WHO classification.

Clinical, qualitative imaging biomarkers, and tumor oxygenation imaging biomarkers for differentiation of midline-located IDH wild-type glioblastomas and H3 K27-altered diffuse midline gliomas in adults

PURPOSE

To compare the clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics of midline-located IDH-wildtype glioblastomas (GBMs) and H3 K27-altered diffuse midline gliomas (DMGs) in adults.

METHODS

Preoperative MRI data of 55 adult patients with midline-located IDH-wildtype GBM or H3 K27-altered DMG (32 IDH-wildtype GBM and 23 H3 K27-altered DMG patients) were included. Qualitative imaging assessment was performed. Quantitative imaging assessment including the tumor volume, normalized cerebral blood volume, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen values, and mean ADC value were performed from the tumor mask via automatic segmentation. Univariable and multivariable logistic analyses were performed.

RESULTS

On multivariable analysis, age (odds ratio [OR] = 0.92, P = 0.015), thalamus or medulla location (OR = 10.48, P = 0.013), presence of necrosis (OR = 0.15, P = 0.038), and OEF (OR = 0.01, P = 0.042) were independent predictors to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.88 (95 % confidence interval: 0.77-0.95), 81.8 %, 82.6 %, and 81.3 %, respectively.

CONCLUSIONS

Along with younger age, tumor location, less frequent necrosis, and lower OEF may be useful imaging biomarkers to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. Tumor oxygenation imaging biomarkers may reflect the less hypoxic nature of H3 K27-altered DMG than IDH-wildtype GBM and may contribute to differentiation.

Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study

PURPOSE

This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients.

METHODS

Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability.

RESULTS

A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70).

CONCLUSIONS

An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study

RATIONALE AND OBJECTIVES

The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs).

MATERIALS AND METHODS

We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed.

RESULTS

A total of 475 patients (DGs: n = 338, CAGs: n = 137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, seven features were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959.

CONCLUSION

Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.

Presurgical MRI-Based Radiomics Models for Predicting Cerebellar Mutism Syndrome in Children With Posterior Fossa Tumors

BACKGROUND

Current studies have indicated that tumoral morphologic features are associated with cerebellar mutism syndrome (CMS), but the radiomics application in CMS is scarce.

PURPOSE

To develop a model for CMS discrimination based on multiparametric MRI radiomics in patients with posterior fossa tumors.

STUDY TYPE

Retrospective.

POPULATION

A total of 218 patients (males 132, females 86) with posterior fossa tumors, 169 of which were included in the MRI radiomics analysis. The MRI radiomics study cohort (169) was split into training (119) and testing (50) sets with a ratio of 7:3.

FIELD/SEQUENCE

All the MRI were acquired under 1.5/3.0 T scanners. T2-weighted image (T2W), T1-weighted (T1W), fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI).

ASSESSMENT

Apparent diffusion coefficient (ADC) maps were generated from DWI. Each MRI dataset generated 1561 radiomics characteristics. Feature selection was performed with univariable logistic analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO) penalized logistic regression. Significant clinical features were selected with multivariable logistic analysis and used to constructed the clinical model. Radiomics models (based on T1W, T2W, FLAIR, DWI, ADC) were constructed with selected radiomics features. The mix model was based on the multiparametric MRI radiomics features.

STATISTICAL TEST

Multivariable logistic analysis was utilized during clinical features selection. Models' performance was evaluated using the area under the receiver operating characteristic (AUC) curve. Interobserver variability was assessed using Cohen's kappa. Significant threshold was set as P < 0.05.

RESULTS

Sex (aOR = 3.72), tumor location (aOR = 2.81), hydrocephalus (aOR = 2.14), and tumor texture (aOR = 5.08) were significant features in the multivariable analysis and were used to construct the clinical model (AUC = 0.79); totally, 33 radiomics features were selected to construct radiomics models (AUC = 0.63-0.93). Seven of the 33 radiomics features were selected for the mix model (AUC = 0.93).

DATA CONCLUSION

Multiparametric MRI radiomics may be better at predicting CMS than single-parameter MRI models and clinical model.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

2.

A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging

PURPOSE

To review eXplainable Artificial Intelligence/(XAI) methods available for medical imaging/(MI).

METHOD

A scoping review was conducted following the Joanna Briggs Institute's methodology. The search was performed on Pubmed, Embase, Cinhal, Web of Science, BioRxiv, MedRxiv, and Google Scholar. Studies published in French and English after 2017 were included. Keyword combinations and descriptors related to explainability, and MI modalities were employed. Two independent reviewers screened abstracts, titles and full text, resolving differences through discussion.

RESULTS

228 studies met the criteria. XAI publications are increasing, targeting MRI (n = 73), radiography (n = 47), CT (n = 46). Lung (n = 82) and brain (n = 74) pathologies, Covid-19 (n = 48), Alzheimer's disease (n = 25), brain tumors (n = 15) are the main pathologies explained. Explanations are presented visually (n = 186), numerically (n = 67), rule-based (n = 11), textually (n = 11), and example-based (n = 6). Commonly explained tasks include classification (n = 89), prediction (n = 47), diagnosis (n = 39), detection (n = 29), segmentation (n = 13), and image quality improvement (n = 6). The most frequently provided explanations were local (78.1 %), 5.7 % were global, and 16.2 % combined both local and global approaches. Post-hoc approaches were predominantly employed. The used terminology varied, sometimes indistinctively using explainable (n = 207), interpretable (n = 187), understandable (n = 112), transparent (n = 61), reliable (n = 31), and intelligible (n = 3).

CONCLUSION

The number of XAI publications in medical imaging is increasing, primarily focusing on applying XAI techniques to MRI, CT, and radiography for classifying and predicting lung and brain pathologies. Visual and numerical output formats are predominantly used. Terminology standardisation remains a challenge, as terms like "explainable" and "interpretable" are sometimes being used indistinctively. Future XAI development should consider user needs and perspectives.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. This Part 2 review focuses on the molecular diagnostics and imaging findings of pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. Each tumor type in pediatric-type diffuse high-grade glioma mostly harbors a distinct molecular marker. On the other hand, in pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas, molecular diagnostics may be extremely complicated at a glance in the 2021 WHO classification. It is crucial for radiologists to understand the molecular diagnostics and imaging findings and leverage the knowledge in clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review

PURPOSE

Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction.

METHOD

The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

RESULTS

Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability.

CONCLUSIONS

The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI

BACKGROUND AND PURPOSE

Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T₁ perfusion MRI parameters combined under a machine learning framework.

MATERIALS AND METHODS

This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T₁ perfusion and DWI data along with conventional MRI images. Analysis of T₁ perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T₁ perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T₁ perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter.

RESULTS

T₁ perfusion parameter values were significantly different between the two groups. The combination of T₁ perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error.

CONCLUSION

The approach of using quantitative T₁ perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

Qualitative and Quantitative Magnetic Resonance Imaging Phenotypes May Predict CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytomas: A Multicenter Study

OBJECTIVE

Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas.

MATERIALS AND METHODS

Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively.

CONCLUSION

The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.

Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients

Background

Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients.

Methods

The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models.

Results

The radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only.

Conclusion

We successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.

An interpretable radiomics model for the diagnosis of panic disorder with or without agoraphobia using magnetic resonance imaging

BACKGROUND

Early and accurate diagnosis of panic disorder with or without agoraphobia (PDA) is crucial to reducing disease burden and individual suffering. However, its diagnosis is challenging for lack of validated biomarkers. This study aimed to investigate whether radiomic features extracted from T1-weighted images (T1) of major fear-circuit structures (amygdala, insula, and anterior cingulate cortex [ACC]) could differentiate patients with PDA from healthy controls (HCs).

METHODS

The 213 participants (93 PDA, 120 HCs) were allocated to training (n = 149) and test (n = 64) sets after undergoing magnetic resonance imaging. Radiomic features (n = 1498) were extracted from T1 of the studied structures. Machine learning models were trained after feature selection and then validated in the test set. SHapley Additive exPlanations (SHAP) explored the model interpretability.

RESULTS

We identified 29 radiomic features to differentiate participants with PDA from HCs. The area under the curve, accuracy, sensitivity, and specificity of the best performing radiomics model in the test set were 0.84 (95% confidence interval: 0.74-0.95), 81.3%, 75.0%, and 86.1%, respectively. The SHAP model explanation suggested that the energy features extracted from the bilateral long insula gyrus and central sulcus of the insula and right ACC were highly associated with the risk of PDA.

LIMITATIONS

This was a cross-sectional study with a relatively small sample size, and the causality of changes in radiomic features and their biological and clinical meanings remained to be elucidated.

CONCLUSIONS

Our findings suggest that radiomic features from the fear-circuit structures could unveil hidden microstructural aberrations underlying the pathogenesis of PDA that could help identify PDA.