Tumor volume and calcifications as indicators for preoperative differentiation of grade II/III diffuse gliomas.

PURPOSE: To retrospectively evaluate preoperative clinical factors for their ability to preoperatively differentiate malignancy grades in patients with incipient supratentorial grade II/III diffuse gliomas. METHODS: This retrospective study included 206 adult patients with incipient supratentorial grade II/III diffuse gliomas according to the 2016 World Health Organization classification of tumors of the central nervous system. The cohort included 136 men and 70 women, with a median age of 41 years. Preoperative factors included age, sex, presence of calcifications on computed tomography scans, and preoperative tumor volume measured using preoperative magnetic resonance imaging. RESULTS: In patients with oligodendrogliomas (IDH-mutant and 1p/19q-codeleted), calcifications were significantly more frequent (p = 0.0034) and tumor volume was significantly larger (p < 0.001) in patients with grade III tumors than in those with grade II tumors. Moreover, in patients with IDH-mutant astrocytomas, preoperative tumor volume was significantly larger (p = 0.0042) in patients with grade III tumors than in those with grade II tumors. In contrast, none of the evaluated preoperative clinical factors were significantly different between the patients with grade II and III IDH-wildtype astrocytomas. CONCLUSION: In adult patients with suspicison incipient supratentorial grade II/III diffuse gliomas, presence of calcifications and larger preoperative tumor volume might be used as preoperative indices to differentiate between malignancy grades II and III in oligodendrogliomas (IDH-mutant and 1p/19q-codeleted) and larger preoperative tumor volume might have similar utility in IDH-mutant astrocytomas.

Prediction of lower-grade glioma molecular subtypes using deep learning.

INTRODUCTION: It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS: A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS: The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS: A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype.

Tumor recurrence patterns after surgical resection of intracranial low-grade gliomas.

INTRODUCTION: Tumor recurrence patterns after resection of intracranial low-grade gliomas (LGG) generally remain obscured. The objective of the present retrospective study was their multifaceted analysis, evaluation of associated factors, and assessment of impact on prognosis. METHODS: Study group comprised 81 consecutive adult patients (46 men and 35 women; median age, 37 years) with recurrent diffuse astrocytomas (DA; 51 cases) and oligodendrogliomas (OD; 30 cases). The median length of follow-up after primary surgery was 6.7 years. RESULTS: Early (within 2 years after primary surgery) and non-early (> 2 years after primary surgery) recurrence was noted in 23 (28%) and 58 (72%) cases, respectively. Fast (≤ 6 months) and slow ( > 6 months) radiological progression of relapse was noted in 31 (38%) and 48 (59%) cases, respectively. Tumor recurrence was local and non-local in 71 (88%) and 10 (12%) cases, respectively. Recurrence patterns have differed in OD, IDH1-mutant DA, and IDH wild-type DA. Early onset, fast radiological progression, and non-local site of relapse had statistically significant negative impact on overall survival of patients and were often associated with malignant transformation of the tumor (38 cases). However, in subgroup with extent of resection ≥ 90% (56 cases) no differences in recurrence characteristics were found between 3 molecularly defined groups of LGG. CONCLUSIONS: Recurrence patterns after resection of LGG show significant variability, differ in distinct molecularly defined types of tumors, and demonstrate definitive impact on prognosis. Aggressive resection at the time of primary surgery may result in more favorable characteristics of recurrence at the time of its development.