IDH1 mutation analysis in low cellularity specimen: a limitation of diagnostic accuracy and a proposal for the diagnostic procedure

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.

A Simple Panel of IDH1 and P53 in Differential Diagnosis Between Low-Grade Astrocytoma and Reactive Gliosis

Background

Reactive gliosis is a response of glial tissue to different types of injury such as brain abscess, trauma, hemorrhage, or even neoplastic process. In some circumstances, especially when the tissue biopsy is small, there may be difficulty to discriminate this reactive condition with low-grade diffuse astrocytoma (World Health Organization [WHO] grade II) by conventional hematoxylin and eosin (H&E) slides, so some immunohistochemical and molecular markers have been introduced for this differential diagnosis. One of the important aspects of updated WHO classification in 2016 has been dividing some of the glial tumor according to IDH1 (isocitrate dehydrogenase 1) mutation.

Objectives

In this study, we tried to evaluate IDH1 and P53 mutation by immunohistochemistry as a simple and highly specific and sensitive method to differentiate low-grade astrocytoma and reactive gliosis.

Material and methods

For 5 years (2013-2018), 50 cases of clinically documented reactive gliosis and 50 cases of low-grade astrocytoma were evaluated for the presence or absence of IDH1 and P53 mutation by immunohistochemistry.

Results

Isocitrate dehydrogenase 1 was positive in 92% and 4% of the astrocytoma and reactive gliosis cases and P53 was positive in 90% and 4% of the cases with the final diagnosis of astrocytoma and reactive gliosis, respectively.

Discussion and conclusion

Combination of P53 and IDH1 as an immunohistochemical panel showed specificity of 96% and sensitivity of 91% for differential diagnosis of reactive gliosis and low-grade astrocytoma. These 2 markers can be extremely helpful for this differential diagnosis.

MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas

BACKGROUND AND PURPOSE

Isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas (histologic grades II and III) with epidermal growth factor receptor (EGFR) amplification or telomerase reverse transcriptase (TERT) promoter mutation are reported to behave similar to glioblastoma. We aimed to evaluate whether MR imaging features could identify a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.

MATERIALS AND METHODS

In this multi-institutional retrospective study, pathologically confirmed IDH wild-type lower-grade gliomas from 2 tertiary institutions and The Cancer Genome Atlas constituted the training set (institution 1 and The Cancer Genome Atlas, 64 patients) and the independent test set (institution 2, 57 patients). Preoperative MRIs were analyzed using the Visually AcceSAble Rembrandt Images and radiomics. The molecular glioblastoma status was determined on the basis of the presence of EGFR amplification and TERT promoter mutation. Molecular glioblastoma was present in 73.4% and 56.1% in the training and test sets, respectively. Models using clinical, Visually AcceSAble Rembrandt Images, and radiomic features were built to predict the molecular glioblastoma status in the training set; then they were validated in the test set.

RESULTS

In the test set, a model using both Visually AcceSAble Rembrandt Images and radiomic features showed superior predictive performance (area under the curve = 0.854) than that with only clinical features or Visually AcceSAble Rembrandt Images (areas under the curve = 0.514 and 0.648, respectively; P < . 001, both). When both Visually AcceSAble Rembrandt Images and radiomics were added to clinical features, the predictive performance significantly increased (areas under the curve = 0.514 versus 0.863, P < .001).

CONCLUSIONS

MR imaging features integrated with machine learning classifiers may predict a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

OBJECTIVES

Isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas of histologic grades II and III follow heterogeneous clinical outcomes, which necessitates risk stratification. We aimed to evaluate whether radiomics from MRI would allow prediction of overall survival in patients with IDHwt lower-grade gliomas and to investigate the added prognostic value of radiomics over clinical features.

METHODS

Preoperative MRIs of 117 patients with IDHwt lower-grade gliomas from January 2007 to February 2018 were retrospectively analyzed. The external validation cohort consisted of 33 patients from The Cancer Genome Atlas. A total of 182 radiomic features were extracted. Radiomics risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator (LASSO) and elastic net. Multivariable Cox regression analyses, including clinical features and RRSs, were performed. The integrated areas under the receiver operating characteristic curves (iAUCs) from models with and without RRSs were calculated for comparisons. The prognostic value of RRS was assessed in the validation cohort.

RESULTS

The RRS derived from LASSO and elastic net independently predicted survival with hazard ratios of 9.479 (95% confidence interval [CI], 3.220-27.847) and 6.148 (95% CI, 3.009-12.563), respectively. Those RRSs enhanced model performance for predicting overall survival (iAUC increased to 0.780-0.797 from 0.726), which was externally validated. The RRSs stratified IDHwt lower-grade gliomas in the validation cohort with significantly different survival.

CONCLUSION

Radiomics has the potential for noninvasive risk stratification and can improve prediction of overall survival in patients with IDHwt lower-grade gliomas when integrated with clinical features.

KEY POINTS

• Isocitrate dehydrogenase wild-type lower-grade gliomas with histologic grades II and III follow heterogeneous clinical outcomes, which necessitates further risk stratification. • Radiomics risk scores derived from MRI independently predict survival even after incorporating strong clinical prognostic features (hazard ratios 6.148-9.479). • Radiomics risk scores derived from MRI have the potential to improve survival prediction when added to clinical features (integrated areas under the receiver operating characteristic curves increased from 0.726 to 0.780-0.797).

MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas

PURPOSE

To identify significant prognostic magnetic resonance imaging (MRI) features and their prognostic value when added to clinical features in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas.

MATERIALS AND METHODS

Preoperative MR images of 158 patients (discovery set = 112, external validation set = 46) with IDHwt lower-grade gliomas (WHO grade II or III) were retrospectively analyzed using the Visually Accessible Rembrandt Images feature set. Radiologic risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator and elastic net. Multivariable Cox regression analysis, including age, Karnofsky Performance score, extent of resection, WHO grade, and RRS, was performed. The added prognostic value of RRS was calculated by comparing the integrated area under the receiver operating characteristic curve (iAUC) between models with and without RRS.

RESULTS

The presence of cysts, pial invasion, and cortical involvement were favorable prognostic factors, while ependymal extension, multifocal or multicentric distribution, nonlobar location, proportion of necrosis > 33%, satellites, and eloquent cortex involvement were significantly associated with worse prognosis. RRS independently predicted survival and significantly enhanced model performance for survival prediction when integrated to clinical features (iAUC increased to 0.773-0.777 from 0.737), which was successfully validated on the validation set (iAUC increased to 0.805-0.830 from 0.735).

CONCLUSION

MRI features associated with prognosis in patients with IDHwt lower-grade gliomas were identified. RRSs derived from MRI features independently predicted survival and significantly improved performance of survival prediction models when integrated into clinical features.

KEY POINTS

• Comprehensive analysis of MRI features conveys prognostic information in patients with isocitrate dehydrogenase wild-type lower-grade gliomas. • Presence of cysts, pial invasion, and cortical involvement of the tumor were favorable prognostic factors. • Radiological phenotypes derived from MRI independently predict survival and have the potential to improve survival prediction when added to clinical features.

Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status

PURPOSE

To evaluate whether diffusion tensor imaging (DTI) radiomics with machine learning improves the prediction of isocitrate dehydrogenase (IDH) mutation status of lower-grade gliomas beyond radiomic features from conventional MRI and DTI histogram parameters.

METHODS

A total of 168 patients with pathologically confirmed lower-grade gliomas were retrospectively enrolled. A total of 158 and 253 radiomic features were extracted from DTI (DTI radiomics) and conventional MRI (T1-weighted image with contrast enhancement, T2-weighted image, and FLAIR [conventional radiomics]), respectively. The random forest models for predicting IDH status were trained with variable combinations as follows: (1) DTI radiomics, (2) conventional radiomics, (3) conventional radiomics + DTI radiomics, and (4) conventional radiomics + DTI histogram. The models were validated with nested cross-validation. The predictive performances of those models were compared by using area under the curve (AUC) from receiver operating characteristic analysis, and 95% confidence interval (CI) was calculated.

RESULTS

Adding DTI radiomics to conventional radiomics significantly improved the accuracy of IDH status subtyping (AUC, 0.900 [95% CI, 0.855-0.945], p = 0.006), whereas adding DTI histogram parameters yielded nonsignificant trend toward improvement (0.869 [95% CI, 0.816-0.922], p = 0.150) compared with the model with conventional radiomics alone (0.835 [95% CI, 0.773-0.896]). The performance of the model consisting of both DTI and conventional radiomics was significantly superior than that of model consisting of both DTI histogram parameters and conventional radiomics (0.900 vs 0.869, p = 0.040).

CONCLUSION

DTI radiomics with machine learning can help improve the subtyping of IDH status beyond conventional radiomics and DTI histogram parameters in patients with lower-grade gliomas.

Amide proton transfer imaging might predict survival and IDH mutation status in high-grade glioma

OBJECTIVES

To assess the utility of amide proton transfer (APT) imaging as an imaging biomarker to predict prognosis and molecular marker status in high-grade glioma (HGG, WHO grade III/IV).

METHODS

We included 71 patients with pathologically diagnosed HGG who underwent preoperative MRI with APT imaging. Overall survival (OS) and progression-free survival (PFS) according to APT signal, clinical factors, MGMT methylation status, and IDH mutation status were analyzed. Multivariate Cox regression models with and without APT signal data were constructed. Model performance was compared using the integrated AUC (iAUC). Associations between APT signals and molecular markers were assessed using the Mann-Whitney test.

RESULTS

High APT signal was a significant predictor for poor OS (HR = 3.21, 95% CI = 1.62-6.34) and PFS (HR = 2.22, 95% CI = 1.33-3.72) on univariate analysis. On multivariate analysis, high APT signals were an independent predictor of poor OS and PFS when clinical factors alone (OS: HR = 2.89; PFS: HR = 2.13), or in combination with molecular markers (OS: HR = 2.85; PFS: HR = 2.00), were included as covariates. The incremental prognostic value of APT signals was significant for OS and PFS. IDH-wild type was significantly associated with high APT signals (p = 0.001) when compared to IDH-mutant; however, there was no difference based on MGMT methylation status (p = 0.208).

CONCLUSION

High APT signal was a significant predictor of poor prognosis in HGG. APT data showed significant incremental prognostic value over clinical prognostic factors and molecular markers and may also predict IDH mutation status.

KEY POINTS

• Amide proton transfer (APT) imaging is a promising prognostic marker of high-grade glioma. • APT signals were significantly higher in IDH-wild type compared to IDH-mutant high-grade glioma. • APT imaging may be valuable for preoperative screening and treatment guidance.

Development of a robust and sensitive pyrosequencing assay for the detection of IDH1/2 mutations in gliomas

Assessment of the mutational status of the isocitrate dehydrogenase 1/2 (IDH1/2) gene has become an integral part of the standard diagnostic procedure and, therefore, needs to be accurate. This may, however, be compromised by various factors including the method of analysis and a low tumor cell content. We have developed a rapid, sensitive and robust assay to detect all types of mutation in either IDH1 or IDH2 using pyrosequencing. The efficacy of detecting mutation was evaluated using a panel of control plasmids representing all the different types of IDH1/2 mutation and a set of 160 tumor specimens. The sensitivity of the assays was examined by a serial dilution analysis performed on samples containing various ratios of wild-type and mutant alleles. The pyrosequencing assay detected as little as 5 % of mutant alleles for most mutation types, while conventional Sanger sequencing required the presence of at least 20 % of mutant alleles for identifying mutations. The pyrosequencing assay detected IDH1/2 mutations in three samples which were missed by Sanger sequencing due to their low tumor cell contents. Our assay is particularly useful for the analysis of a large number of specimens as in a retrospective clinical study for example.