Direct image to subtype prediction for brain tumors using deep learning

BACKGROUND: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. METHODS: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. RESULTS: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. CONCLUSIONS: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available

Rapidly progressive dementia with thalamic degeneration and peculiar cortical prion protein immunoreactivity, but absence of proteinase K resistant PrP: a new disease entity?

BACKGROUND: Human prion diseases are a group of rare fatal neurodegenerative conditions with well-developed clinical and neuropathological diagnostic criteria. Recent observations have expanded the spectrum of prion diseases beyond the classically recognized forms. RESULTS: In the present study we report six patients with a novel, apparently sporadic disease characterised by thalamic degeneration and rapidly progressive dementia (duration of illness 2-12 months; age at death: 55-81 years). Light and electron microscopic immunostaining for the prion protein (PrP) revealed a peculiar intraneuritic distribution in neocortical regions. Proteinase K resistant PrP (PrPres) was undetectable by Western blotting in frontal cortex from the three cases with frozen tissue, even after enrichment for PrPres by centrifugation or by phosphotungstic acid precipitation. Conformation-dependent immunoassay analysis using a range of PK digestion conditions (and no PK digestion) produced only very limited evidence of meaningful D-N (denatured/native) values, indicative of the presence of disease-associated PrP (PrPSc) in these cases, when the results were compared with appropriate negative control groups. CONCLUSIONS: Our observation expands the spectrum of conditions associated with rapidly progressive dementia and may have implications for the understanding of the pathogenesis of prion diseases

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated.

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

memo - Magazine of European Medical Oncology / Anti-angiogenic therapies in brain metastases

Brain metastases are a major challenge in modern oncology, as treatment options upon the diagnosis of symptomatic brain metastases are limited. Neo-angiogenesis was identified as a hallmark of brain metastasis development and inhibition using anti-angiogenic therapy might therefore be an experimental promising preventive as well as therapeutic approach. The current review will summarize the current available data on the efficacy of neo-angiogenic therapies in patients with brain metastases.(VLID)357543