Rationale and Objectives This study aimed to develop and validate a deep learning-based brain metastasis detection model (BMDM) in magnetic resonance images for diagnosing brain metastases (BMs). Materials and Methods We retrospectively collected data from 950 patients serving as the training and test sets for developing BMDM and from an additional 423 patients as the validation set. Three reading modes were compared: radiologists only (10 total, four with ≤3 years of experience and six with >3 years of experience), BMDM only, and radiologists assisted by the BMDM. The alternative free-response receiver operating characteristic (AFROC) method was used for evaluation. Results The reading time was reduced by 30.87%, AFROC-area under the curve improved from 0.837 to 0.954, and sensitivity increased from 0.685 to 0.916 with BMDM assistance. The improvement in sensitivity was more pronounced among less experienced radiologists (24.59% vs 22.03%). The detection sensitivity improved by 33.45% for lesions ≤3 mm and by 43.00% for insular lesions. Conclusion The results demonstrated that BMDM significantly enhanced time efficiency and diagnostic performance for BM detection, providing clinical benefits.

To develop a non‑invasive multiparametric MRI radiomics model for predicting IDH mutation and MGMT promoter methylation in adult diffuse gliomas, systematically evaluating different peritumoural regions (3 mm, 5 mm, 10 mm) and sequence combinations contrast-enhanced T1-weighted imaging (T1WI-CE), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and arterial spin labeling (ASL) with multi‑centre external validation. Data from two centres were analysed (UCSF: IDH n = 295, MGMT n = 266; AHHU eligible cohorts: IDH n = 34, MGMT n = 25). The UCSF cohort was stratified into training (80%) and internal test (20%) sets. After model development, the complete pipeline was repeated on all UCSF cases to fit each final model. Feature-complete AHHU subsets were used for the reported external results (IDH n = 24; MGMT n = 22). After training-only imputation and Z-score scaling, radiomic features were screened using ICC, Mann-Whitney U testing, Spearman correlation filtering, a hybrid mRMR procedure, and L1-penalised logistic regression. Five-fold cross-validation within the training set selected the regularisation parameter from 10 logarithmically spaced candidate C values. The locked development models were evaluated on the internal test set, and the full-UCSF refits were evaluated on the external validation set (AHHU). In exploratory external-cohort comparisons, the 5 mm peritumoural T2-FLAIR + ASL model showed the highest observed AUC for IDH prediction (0.977, 95% CI 0.900-1.000), exceeding the 10 mm ASL model (p < 0.001) and the 3 mm T1WI-CE + T2-FLAIR model (p = 0.0229). For MGMT prediction, the 3 mm T1WI-CE + T2-FLAIR + ASL model yielded an AUC of 0.800 (95% CI 0.500-1.000). In paired-bootstrap comparisons, it exceeded the tumour-core T2-FLAIR model (p = 0.018) but not the 5 mm T2-FLAIR model (p = 0.110). These between-model external comparisons were hypothesis-generating and were not used to refit any model. Peritumoural radiomics showed promising performance for IDH prediction. The 5 mm region had the highest observed performance for IDH in this cohort, whereas the apparent advantage of the 3 mm model for MGMT remains exploratory because of the small external sample and lack of consistent superiority across comparators. Independent validation in larger cohorts is required.

PURPOSE:The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma. MATERIALS AND METHODS:This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent. Habitat radiomics features were extracted from tumor subregions by k-means clustering, while deep learning features were acquired using a 3D convolutional neural network. Model performance was evaluated based on area under the curve (AUC) value, F1-score, and decision curve analysis. RESULTS:The combined model integrating clinical data, conventional radiomics, habitat imaging features, and deep learning achieved the highest performance (training AUC = 0.979 [95 % CI: 0.969-0.990], F1-score = 0.944; testing AUC = 0.777 [0.651-0.904], F1-score = 0.711). Among the single-modality models, habitat radiomics outperformed the other models (training AUC = 0.960 [0.954-0.983]; testing AUC = 0.724 [0.573-0.875]). CONCLUSION:The proposed multimodal framework considerably enhances preoperative prediction of MGMT gene promoter methylation, with habitat radiomics highlighting the critical role of tumor heterogeneity. This approach provides a scalable tool for personalized management of glioma.

Purpose:To evaluate the predictive value of apparent diffusion coefficient (ADC) for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and its correlation with Ki-67 proliferation index in adult-type diffuse glioma, stratified by isocitrate dehydrogenase (IDH) subtype. Methods:This retrospective study enrolled 94 patients with pathologically confirmed glioma (2017-2024). ADCmin, ADCmean, and relative ADC (rADC) values were derived from diffusion-weighted imaging (b=1000 s/mm²). MGMT methylation, IDH mutation, and Ki-67 index were assessed by Pyrosequencing and immunohistochemistry. Receiver operating curve analysis was performed to evaluate diagnostic performance, and Spearman correlation was used to link ADC with Ki-67 index. Results:MGMT-methylated gliomas exhibited significantly higher ADCmin (0.86 vs. 0.74 × 10-³mm²/s, p = 0.013) and rADCmin (1.12 vs. 0.95, p<0.001). In the IDH-wild-type subgroup, rADCmin achieved an AUC of 0.78 (cutoff=1.10, sensitivity=80.0%). All ADC parameters were negatively correlated with Ki-67 (ρ=- 0.32 to -0.24, p<0.05). Conclusion:ADC values, particularly rADCmin, were identified as non-invasive biomarkers for MGMT methylation prediction in IDH-wild-type gliomas. An inverse correlation between ADC and Ki-67 index supported their utility for assessing tumor proliferation. Standardizing rADC would improve its clinical applicability across imaging platforms.

IntroductionThis study was designed to explore the feasibility of semiautomatic measurement of abnormal signal volume (ASV) in glioblastoma (GBM) patients, and the predictive value of ASV evolution for the survival prognosis after chemoradiotherapy (CRT).MethodsThis retrospective trial included 110 consecutive patients with GBM. MRI metrics, including the orthogonal diameter (OD) of the abnormal signal lesions, the pre-radiation enhancement volume (PRRCE), the volume change rate of enhancement (rCE), and fluid attenuated inversion recovery (rFLAIR) before and after CRT were analyzed. Semi-automatic measurements of ASV were done through the Slicer software.ResultsIn logistic regression analysis, age (HR = 2.185, p = 0.012), PRRCE (HR = 0.373, p < 0.001), post CE volume (HR = 4.261, p = 0.001), rCE(1m) (HR = 0.519, p = 0.046) were the significant independent predictors of short overall survival (OS) (< 15.43 months). The areas under the receiver operating characteristic curve (AUCs) for predicting short OS with rFLAIR(3m) and rCE(1m) were 0.646 and 0.771, respectively. The AUCs of Model 1 (clinical), Model 2 (clinical + conventional MRI), Model 3 (volume parameters), Model 4 (volume parameters + conventional MRI), and Model 5 (clinical + conventional MRI + volume parameters) for predicting short OS were 0.690, 0.723, 0.877, 0.879, 0.898, respectively.ConclusionSemi-automatic measurement of ASV in GBM patients is feasible. The early evolution of ASV after CRT was beneficial in improving the survival evaluation after CRT. The efficacy of rCE(1m) was better than that of rFLAIR(3m) in this evaluation.

病例资料 患者,男,66岁.3个月前无明显诱因出现走路不稳,反应迟钝,小便失禁,无恶心呕吐,无发热抽搐.患者自发病以来精神较差,饮食及睡眠不良,体重稍减轻.专科体检:四肢肌力及肌张力正常,膝腱反射正常,双侧巴氏征阴性,克氏征阴性.常规实验室检查及胸片无异常.

患者 男,38岁.无明显诱因出现无痛性全程肉眼血尿1月余,色鲜红,无血块,伴腰部绞痛不适以及间断性排尿困难.肛门指诊:前列腺右侧触及一"花生"大小结节,无触痛.尿常规:小圆上皮细胞增多(3.10 μL),尿白细胞(+).血常规:白细胞计数增高(10.70×109/L).血总前列腺特异抗原正常(1.77 ng/ml).其他常规实验室检查及胸部X线平片未见明显异常.

Aims To investigate whether the relative signal intensity surrounding the residual cavity on T2-fluid-attenuated inversion recovery (rFLAIR) can improve the survival prediction of lower-grade glioma (LGG) patients. Methods Clinical and pathological data and the follow-up MR imaging of 144 patients with LGG were analyzed. We calculated rFLAIR with Image J software. Logistic analysis was used to explore the significant impact factors on progression-free survival (PFS) and overall survival (OS). Several models were set up to predict the survival prognosis of LGG. Results A higher rFLAIR [1.81 (0.83)] [median (IQR)] of non-enhancing regions surrounding the residual cavity was detected in the progressed group (n=77) than that [1.55 (0.33)] [median (IQR)] of the not-progressed group (n = 67) (P<0.001). Multivariate analysis showed that lower KPS (≤75), and higher rFLAIR (>1.622) were independent predictors for poor PFS (P<0.05), whereas lower KPS (≤75) and thick-linear and nodular enhancement were the independent predictors for poor OS (P<0.05). The cutoff rFLAIR value of 1.622 could be used to predict poor PFS (HR = 0.31, 95%CI 0.20–0.48) (P<0.001) and OS (HR = 0.27, 95%CI 0.14–0.51) (P=0.002). Both the areas under the ROC curve (AUCs) for predicting poor PFS (AUC, 0.771) and OS (AUC, 0.831) with a combined model that contained rFLAIR were higher than those of any other models. Conclusion Higher rFALIR (>1.622) in non-enhancing regions surrounding the residual cavity can be used as a biomarker of the poor survival of LGG. rFLAIR is helpful to improve the survival prediction of posttreatment LGG patients.

Purpose T2-Fluid attenuated inversion recovery (FLAIR)hyperintensityoutside the residual cavity is one of the important MRI features in lowergrade gliomas (LGG), but its prognostic value needs to be further explored. The purpose of thisstudy was to investigate whether the relative signal intensity of T2-FLAIR outside the residual cavity (rFLAIR) can improve survival predictionofpost-treatment LGG patients or not. Methods Clinical and pathological data, and early follow-up MR imaging of 152 patients with LGG were reviewed.We calculatedrFLAIRwith Image J software.Logistic analysiswas used to explore the significant clinicaland conventional MRI factors, and rFLAIR on progressionfree survival (PFS) and overall survival (OS).Different models were setup to predict survival prognosis of LGG. Results Higher rFLAIR (1.80±0.84) of non-contrast-enhancing lesionsoutside residual cavity was detected in progression group ( n =80) than that (1.55±0.33) of non-progression group ( n = 72) ( P <0.001)after radiotherapy.Multivariate analysis showed that higher rFLAIR(>1.595), as well as thick-linear and nodular enhancement of the residual cavity wall, were independent factors for the poor PFS and OS (both P <0.05). The cut-offrFLAIRof 1.595 could be used to predict poor PFS(HR 0.27, 95%CI 0.17-0.42) and OS (HR 0.22，95%CI 0.12-0.40)( P <0.001).Areas under the ROC curve (AUCs) for predicting poor PFS: clinical model 0.726, conventional MRI model 0.672, clinical + conventional MRI model 0.760, clinical + conventional MRI + rFLAIR combined model 0.827; AUCs for predicting poorer OS: clinical model 0.799, conventional MRI model 0.735, clinical + conventional MRI model 0.843, clinical + conventional MRI + rFLAIR combined model 0.880. Conclusions Our preliminary results indicated that higherrFALIR (>1.595) of non-contrast-enhancing lesionsoutside the residual cavity can be used as a biomarker of poor survival of LGG. Moreover,rFLAIR is helpful to improve the survival prediction of post-treatment LGG patients.

世界卫生组织(WHO)根据胶质瘤的组织病理学特征将其分为Ⅰ～Ⅳ级,认为Ⅰ级和Ⅱ级胶质瘤预后较好,称为低级别胶质瘤;而Ⅲ级和Ⅳ级者预后不良,合称为高级别胶质瘤.但最近研究发现[1,2],Ⅲ级胶质瘤与Ⅳ级胶质母细胞瘤之间的生物学行为和分子特征不同,主要是异柠檬酸脱氢酶(isocitrate dehydrogenase,IDH)的表型差异,导致预后区别较大,遂提出较低级别胶质瘤(lower-grade glioma,LGG)的概念.LGG包括Ⅱ级和Ⅲ级少突胶质细胞瘤、星形细胞瘤及少突星形细胞瘤.有研究显示[1],IDH突变的Ⅱ级和Ⅲ级星形细胞瘤的发病年龄近似,生存期差别较小,其IDH基因表型突变率及总生存率明显高于胶质母细胞瘤;同时,这种基因表型的差异也会影响其影像学表现以及治疗后生存预后,因此有必要进行LGG专项研究.

病例资料患者,男,64岁,既往体健,半个月前体检头部CT发现"右颞顶叶占位性病变",无头痛、头晕,无局限性神经功能障碍。查体:体温 36.5℃,心率75次/分,呼吸19次/分,血压135/68 mmHg,神清语利,对光反射灵敏,颈软无抵抗,四肢肌力及肌张力正常,膝腱反射正常,双侧巴氏征阴性,Kernig征阴性。常规实验室检查及胸片无异常。MRI平扫（图1～6）:右侧颞顶叶囊实性混杂肿物,大小3.5 cm×4.2 cm×3.5 cm

患者女,53岁.无明显诱因出现排便不尽感,每日排便一次,为成形黄色大便,无黏液及脓血.结肠镜检查:横结肠距肛缘70 cm处见一大小约1.8 cm×0.8 cm隆起性病变,中央凹陷,活检显示肠黏膜组织急慢性炎症及少许肉芽组织.常规实验室检查及胸片检查无异常. CT腹部平扫+增强检查:横结肠近端下壁可见一结节,边界清楚,大小约1.2 cm×1.6 cm,密度均匀,CT值37 HU;增强扫描上述病变明显强化,三期CT值为58 HU、81 HU及89 HU(图1～6).肠系膜根部及腹膜后淋巴结未见增大.