Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data. Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming. With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively. We developed a deep learning technique to noninvasively predict genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. .

Purpose Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions. Methods Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive. Results Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198). Conclusion Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.

Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

Among patients who were not randomly allocated to either approach, eTSS resulted in a higher rate of GTR as compared to mTSS for all patients and for NFPA patients alone, but only in a fixed-effect model. For FPA, however, eTSS did not seem to offer a significantly higher rate of GTR. These conclusions should be interpreted with caution because of the nature of the included non-comparative studies.

OBJECTIVEWhile the effect of increased extent of resection (EOR) on survival in diffuse infiltrating low-grade glioma (LGG) patients is well established, there is still uncertainty about the influence of the new WHO molecular subtypes. The authors designed a retrospective analysis to assess the interplay between EOR and molecular classes.METHODSThe authors retrospectively reviewed the records of 326 patients treated surgically for hemispheric WHO grade II LGG at Brigham and Women’s Hospital and Massachusetts General Hospital (2000–2017). EOR was calculated volumetrically and Cox proportional hazards models were built to assess for predictive factors of overall survival (OS), progression-free survival (PFS), and malignant progression–free survival (MPFS).RESULTSThere were 43 deaths (13.2%; median follow-up 5.4 years) among 326 LGG patients. Median preoperative tumor volume was 31.2 cm3 (IQR 12.9–66.0), and median postoperative residual tumor volume was 5.8 cm3 (IQR 1.1–20.5). On multivariable Cox regression, increasing postoperative volume was associated with worse OS (HR 1.02 per cm3; 95% CI 1.00–1.03; p = 0.016), PFS (HR 1.01 per cm3; 95% CI 1.00–1.02; p = 0.001), and MPFS (HR 1.01 per cm3; 95% CI 1.00–1.02; p = 0.035). This result was more pronounced in the worse prognosis subtypes of IDH-mutant and IDH-wildtype astrocytoma, for which differences in survival manifested in cases with residual tumor volume of only 1 cm3. In oligodendroglioma patients, postoperative residuals impacted survival when exceeding 8 cm3. Other significant predictors of OS were age at diagnosis, IDH-mutant and IDH-wildtype astrocytoma classes, adjuvant radiotherapy, and increasing preoperative volume.CONCLUSIONSThe results corroborate the role of EOR in survival and malignant transformation across all molecular subtypes of diffuse LGG. IDH-mutant and IDH-wildtype astrocytomas are affected even by minimal postoperative residuals and patients could potentially benefit from a more aggressive surgical approach.

Background Breast cancer (BC) brain metastases (BM) can have discordant hormonal or human epidermal growth factor receptor 2 (HER2) expression compared with corresponding primary tumors. This study aimed to describe incidence, predictors, and survival outcomes of discordant receptors and associated subtype switching in BM. Methods BCBM patients seen at 4 tertiary institutions who had undergone BM resection or biopsy were included. Surgical pathology reports were retrospectively assessed to determine discordance between the primary tumor and the BCBM. In discordant cases, expression in extracranial metastases was also assessed. Results In BM from 219 patients, prevalence of any discordance was 36.3%; receptor-specific discordance was 16.7% for estrogen, 25.2% for progesterone, and 10.4% for HER2. Because estrogen and progesterone were considered together for hormonal status, 50 (22.8%) patients switched subtype as a result; 20 of these switches were HER2 based. Baseline subtype predicted switching, which occurred in up to 37.5% of primary HR+ patients. Moreover, 14.8% of initially HER2-negative patients gained HER2 in the BM. Most (63.6%) discordant patients with extracranial metastases also had discordance between BM and extracranial subtype. Loss of receptor expression was generally associated with worse survival, which appeared to be driven by estrogen loss (hazard ratio = 1.80, P = 0.03). Patients gaining HER2 status (n = 8) showed a nonsignificant tendency toward improved survival (hazard ratio = 0.64, P = 0.17). Conclusions In this multicenter study, we report incidence and predictors of subtype switching, the risk of which varies considerably by baseline subtype. Switches can have clinical implications for prognosis and treatment choice. Key Points 1. Breast cancer switches subtype in up to 37.5% of brain metastases, depending on baseline subtype. 2. HER2 gains occur in 14.8% of HER2-negative patients developing brain metastases. 3. Subtype can differ between primary, intracranial, and extracranial distant metastases.

PURPOSE In patients with diffuse low-grade glioma (LGG), the extent of surgical tumor resection (EOR) has a controversial role, in part because a randomized clinical trial with different levels of EOR is not feasible. METHODS In a 20-year retrospective cohort of 392 patients with IDH-mutant grade 2 glioma, we analyzed the combined effects of volumetric EOR and molecular and clinical factors on overall survival (OS) and progression-free survival by recursive partitioning analysis. The OS results were validated in two external cohorts (n = 365). Propensity score analysis of the combined cohorts (n = 757) was used to mimic a randomized clinical trial with varying levels of EOR. RESULTS Recursive partitioning analysis identified three survival risk groups. Median OS was shortest in two subsets of patients with astrocytoma: those with postoperative tumor volume (TV) > 4.6 mL and those with preoperative TV > 43.1 mL and postoperative TV ≤ 4.6 mL. Intermediate OS was seen in patients with astrocytoma who had chemotherapy with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL in addition to oligodendroglioma patients with either preoperative TV > 43.1 mL and residual TV ≤ 4.6 mL or postoperative residual volume > 4.6 mL. Longest OS was seen in astrocytoma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL who received no chemotherapy and oligodendroglioma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL. EOR ≥ 75% improved survival outcomes, as shown by propensity score analysis. CONCLUSION Across both subtypes of LGG, EOR beginning at 75% improves OS while beginning at 80% improves progression-free survival. Nonetheless, maximal resection with preservation of neurological function remains the treatment goal. Our findings have implications for surgical strategies for LGGs, particularly oligodendroglioma.

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