Abstract. Mutations in the isocitrate dehydrogenase 1 and 2 genes (IDH1 and IDH2) appear to occur frequently and selectively in gliomas. Our aim was to assess whether IDH mutations are common in Chinese glioma patients and whether the mutations predict good response to concomitant chemoradiotherapy. In this study IDH1 and IDH2 mutations were detected in a series of 203 gliomas. IDH1 mutations were present in 75 of the 203 cases (36.9%) while IDH2 mutations in 5 of the 203 cases (2.5%). No tumor was mutated in both IDH1 and IDH2. IDH1/2 mutations were associated with prolonged overall survival in the whole series of patients exclusive of pilocytic astrocytoma (P<0.001), WHO grade Ⅱ patients who received no adjuvant therapy after surgery (P= 0.014) and WHO grade Ⅲ patients who received concomitant chemoradiotherapy (standard schedule) after surgery (P=0.033). Furthermore, there was no correlation between IDH1/2 mutations and reponse to concomitant chemoradiotherapy in anaplastic gliomas. Our results suggest that IDH1 mutations also occur freuqently in Chinese glioma patients but the frequency of IDH1 mutations is below the findings reported by North American and European groups. Furthermore, we confirm the prognostic significance of IDH1/2 mutations in gliomas, but the mutations cannot predict a favorable response to concomitant chemoradiotherapy in anaplastic gliomas. IntroductionGliomas are the most frequent and lethal brain tumors and display a wide diversity with location, morphology, genetic status and response to therapy. These tumors have been classified as grade I to grade IV based on histopathological and clinical criteria established by the World Health Organization (WHO) (1). Despite intensive therapies, including surgery, radiotherapy (RT) and chemotherapy (CT), the outcome of glioma patients remain depressing (2,3). Especially, glioblastoma multiforme (GBM), the most prevalent form of brain tumors, has one of the worst prognosis among all types of gliomas with a median progression-free survival (PFS) of 6.9 months and a median overall survival (OS) of 14.6 months through surgery plus standard concomitant chemoradiotherapy (CCRT) (2,4).A combined understanding of the genetic basis and pathology of gliomas provides insight into biologically based tumor classification and identifies molecular prognostic biomarkers. In turn, this information is the route by which the most effective therapy can be focused (5). The latest breakthrough came in 2008, when the gene encoding isocitrate dehydrogenase 1 (IDH1) was initially found to be mutated in approximately 12% of GBM (6) followed by the observation that it was mutated in the majority of WHO grade II and III gliomas (7-11). IDH1 (encoded by IDH1 gene on chromosomal 2q33.3 and located in the cytoplasm and peroxisomes) or its mitochondrial counterpart IDH2, is an enzyme that catalyzes the oxidative decarboxylation of isocitrate to α-ketoglutarate thereby leading to NADPH (Nicotinamide Adenine Dinucleotide Phosphate) production (12). In the vast majority of the c...
ObjectivesTo investigate the prognostic role of radiomic features based on pretreatment MRI in predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC).MethodsAll 181 women with histologically confirmed LACC were randomly divided into the training cohort (n = 126) and the validation cohort (n = 55). For each patient, we extracted radiomic features from whole tumors on sagittal T2WI and axial DWI. The least absolute shrinkage and selection operator (LASSO) algorithm combined with the Cox survival analysis was applied to select features and construct a radiomic score (Rad-score) model. The cutoff value of the Rad-score was used to divide the patients into high- and low-risk groups by the X-tile. Kaplan–Meier analysis and log-rank test were used to assess the prognostic value of the Rad-score. In addition, we totally developed three models, the clinical model, the Rad-score, and the combined nomogram.ResultsThe Rad-score demonstrated good performance in stratifying patients into high- and low-risk groups of progression in the training (HR = 3.279, 95% CI: 2.865–3.693, p < 0.0001) and validation cohorts (HR = 2.247, 95% CI: 1.735–2.759, p < 0.0001). Otherwise, the combined nomogram, integrating the Rad-score and patient’s age, hemoglobin, white blood cell, and lymph vascular space invasion, demonstrated prominent discrimination, yielding an AUC of 0.879 (95% CI, 0.811–0.947) in the training cohort and 0.820 (95% CI, 0.668–0.971) in the validation cohort. The Delong test verified that the combined nomogram showed better performance in estimating PFS than the clinical model and Rad-score in the training cohort (p = 0.038, p = 0.043).ConclusionThe radiomics nomogram performed well in individualized PFS estimation for the patients with LACC, which might guide individual treatment decisions.
Objective: Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. Methods: Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. Results: The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77–0.90) and 0.82 (95% CI: 0.71–0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. Conclusions: The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and II EOC. Advances in knowledge: This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists noninvasively identify EOC types before surgery.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.