Radiomics and PD-L1 expression predict immunotherapy benefits in patients with head and neck squamous cell carcinoma.

Aim: To evaluate the performance of MRI-derived radiomic risk score (RRS) and PD-L1 expression to predict overall survival (OS) and progression-free survival (PFS) of patients with recurrent head and neck squamous cell carcinoma receiving nivolumab therapy. Materials & methods: Three hundred forty radiomic features from pretreatment MRI were used to construct the RRS. The integrated area under the receiver operating characteristic curve (iAUC) was calculated to evaluate the performance of the RRS and PD-L1. Results: The RRS showed iAUCs of 0.69 and 0.57 for OS and PFS, respectively. PD-L1 expression showed iAUCs of 0.61 and 0.62 for OS and PFS, respectively. Conclusion: RRS and PD-L1 potentially predict the OS and PFS of patients with recurrent head and neck squamous cell carcinoma receiving nivolumab therapy.

[Box: see text].

Malignancy risk of indeterminate lymph node at the central compartment in patients with thyroid cancer and concomitant sonographic thyroiditis.

BACKGROUND: To evaluate the malignancy risk of sonographic (US) indeterminate lymph node (LN)s at the central compartment in thyroid cancer patients with US-thyroiditis (ST). METHODS: Among the central compartments of suspicious, indeterminate, and probably benign LN US categories, the malignancy rates were compared between ST and non-US-thyroiditis (non-ST) groups. Those of indeterminate category were compared with suspicious and probably benign categories. RESULTS: At 531 central compartments from 349 patients, the malignancy rate was lower in ST group (34.4% [44/128]) than non-ST group (43.4% [175/403]), although statistically not significant (p = 0.08). The malignancy rate of indeterminate category in ST group (35.7% [5/14]) was lower than non-ST group (71.9% [23/32]) (p = 0.047). Within ST group, the malignancy rate of indeterminate category (35.7% [5/14]) did not differ from probably benign category (29.1% [30/103]) (p = 0.756), but was lower than suspicious category (81.8% [9/11]) (p = 0.042). CONCLUSIONS: The malignancy risk of US indeterminate LNs at the central compartment in thyroid cancer patients with US thyroiditis was lower than that in patients without US thyroiditis.

Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification.

OBJECTIVES: Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification. METHODS: This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor. RESULTS: Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05). CONCLUSIONS: GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended. CLINICAL RELEVANCE STATEMENT: Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas. KEY POINTS: The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.

18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma.

PURPOSE: Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. MATERIALS AND METHODS: Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. RESULTS: A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. CONCLUSION: When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.

OBJECTIVES: Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS: Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS: Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION: MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT: MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS: • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.

OBJECTIVES: To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. RESULTS: A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). CONCLUSIONS: The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. CLINICAL RELEVANCE STATEMENT: Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. KEY POINTS: • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.

Natural language processing to predict isocitrate dehydrogenase genotype in diffuse glioma using MR radiology reports.

OBJECTIVES: To evaluate the performance of natural language processing (NLP) models to predict isocitrate dehydrogenase (IDH) mutation status in diffuse glioma using routine MR radiology reports. MATERIALS AND METHODS: This retrospective, multi-center study included consecutive patients with diffuse glioma with known IDH mutation status from May 2009 to November 2021 whose initial MR radiology report was available prior to pathologic diagnosis. Five NLP models (long short-term memory [LSTM], bidirectional LSTM, bidirectional encoder representations from transformers [BERT], BERT graph convolutional network [GCN], BioBERT) were trained, and area under the receiver operating characteristic curve (AUC) was assessed to validate prediction of IDH mutation status in the internal and external validation sets. The performance of the best performing NLP model was compared with that of the human readers. RESULTS: A total of 1427 patients (mean age ± standard deviation, 54 ± 15; 779 men, 54.6%) with 720 patients in the training set, 180 patients in the internal validation set, and 527 patients in the external validation set were included. In the external validation set, BERT GCN showed the highest performance (AUC 0.85, 95% CI 0.81-0.89) in predicting IDH mutation status, which was higher than LSTM (AUC 0.77, 95% CI 0.72-0.81; p = .003) and BioBERT (AUC 0.81, 95% CI 0.76-0.85; p = .03). This was higher than that of a neuroradiologist (AUC 0.80, 95% CI 0.76-0.84; p = .005) and a neurosurgeon (AUC 0.79, 95% CI 0.76-0.84; p = .04). CONCLUSION: BERT GCN was externally validated to predict IDH mutation status in patients with diffuse glioma using routine MR radiology reports with superior or at least comparable performance to human reader. CLINICAL RELEVANCE STATEMENT: Natural language processing may be used to extract relevant information from routine radiology reports to predict cancer genotype and provide prognostic information that may aid in guiding treatment strategy and enabling personalized medicine. KEY POINTS: • A transformer-based natural language processing (NLP) model predicted isocitrate dehydrogenase mutation status in diffuse glioma with an AUC of 0.85 in the external validation set. • The best NLP models were superior or at least comparable to human readers in both internal and external validation sets. • Transformer-based models showed higher performance than conventional NLP model such as long short-term memory.

Revisiting prognostic factors in glioma with leptomeningeal metastases: a comprehensive analysis of clinical and molecular factors and treatment modalities.

PURPOSE: To comprehensively investigate prognostic factors, including clinical and molecular factors and treatment modalities, in adult glioma patients with leptomeningeal metastases (LM). METHODS: Total 226 patients with LM (from 2001 to 2021 among 1495 grade 2 to 4 glioma patients, 88.5% of LM patients being IDH-wildtype) with complete information on IDH mutation, 1p/19q codeletion, and MGMT promoter methylation status were enrolled. Predictors of overall survival (OS) of entire patients were determined by time-dependent Cox analysis, including clinical, molecular, and treatment data. Subgroup analyses were performed for patients with LM at initial diagnosis and LM diagnosed at recurrence (herein, initial and recurrent LM). Identical analyses were performed in IDH-wildtype glioblastoma patients. RESULTS: Median OS was 17.0 (IQR 9.7-67.1) months, with shorter median OS in initial LM than recurrent LM patients (12.2 vs 20.6 months, P < 0.001). In entire patients, chemotherapy and antiangiogenic therapy were predictors of longer OS, while male sex and initial LM were predictors of shorter OS. In initial LM, higher KPS, chemotherapy, and antiangiogenic therapy were predictors of longer OS, while male sex was a predictor of shorter OS. In recurrent LM, chemotherapy and longer interval between initial glioma and LM diagnoses were predictors of longer OS, while male sex was a predictor of shorter OS. A similar trend was observed in IDH-wildtype glioblastoma. CONCLUSION: Active chemotherapy and antiangiogenic therapy demonstrated survival benefit in glioma patients with LM. There is consistent female survival advantage, whereas longer interval between initial glioma diagnosis and LM development suggests longer OS in recurrent LM.

Development and Testing of a Machine Learning Model Using 18F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.

OBJECTIVE: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. MATERIALS AND METHODS: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. RESULTS: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. CONCLUSION: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

Leptomeningeal metastases in glioma revisited: incidence and molecular predictors based on postcontrast fluid-attenuated inversion recovery imaging.

OBJECTIVE: Leptomeningeal metastases (LMs) in glioma have been underestimated given their low incidence and the lack of reliable imaging. Authors of this study aimed to investigate the real-world incidence of LMs using cerebrospinal fluid (CSF)-sensitive imaging, namely postcontrast fluid-attenuated inversion recovery (FLAIR) imaging, and to analyze molecular predictors for LMs in the molecular era. METHODS: A total of 1405 adult glioma (World Health Organization [WHO] grade 2-4) patients underwent postcontrast FLAIR imaging at initial diagnosis and during treatment monitoring between 2001 and 2021. Collected molecular data included isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion, H3 K27 alteration, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. LM diagnosis was performed with MRI including postcontrast FLAIR sequences. Logistic regression analysis for LM development was performed with molecular, clinical, and imaging data. Overall survival (OS) was compared between patients with and those without LM. RESULTS: LM was identified in 228 patients (16.2%), 110 (7.8%) at the initial diagnosis and 118 (8.4%) at recurrence. Among the molecular diagnostics, IDH-wildtype (OR 3.14, p = 0.001) and MGMT promoter unmethylation (OR 1.43, p = 0.034) were independent predictors of LM. WHO grade 4 (OR 10.52, p < 0.001) and nonlobar location (OR 1.56, p = 0.048) were associated with LM at initial diagnosis, whereas IDH-wildtype (OR 5.04, p < 0.001) and H3 K27 alteration (OR 3.39, p = 0.003) were associated with LM at recurrence. Patients with LM had a worse median OS than those without LM (16.7 vs 32.0 months, p < 0.001, log-rank test), which was confirmed as an independent factor on multivariable Cox analysis (p = 0.004). CONCLUSIONS: CSF-sensitive imaging aids the diagnosis of LM, demonstrating a high incidence of LM in adult gliomas. Furthermore, molecular markers are associated with LM development in glioma, and patients with aggressive molecular markers warrant imaging surveillance for LM.

Three-dimensional fractal dimension and lacunarity features may noninvasively predict TERT promoter mutation status in grade 2 meningiomas.

PURPOSE: The 2021 World Health Organization classification includes telomerase reverse transcriptase promoter (TERTp) mutation status as a factor for differentiating meningioma grades. Therefore, preoperative prediction of TERTp mutation may assist in clinical decision making. However, no previous study has applied fractal analysis for TERTp mutation status prediction in meningiomas. The purpose of this study was to assess the utility of three-dimensional (3D) fractal analysis for predicting the TERTp mutation status in grade 2 meningiomas. METHODS: Forty-eight patients with surgically confirmed grade 2 meningiomas (41 TERTp-wildtype and 7 TERTp-mutant) were included. 3D fractal dimension (FD) and lacunarity values were extracted from the fractal analysis. A predictive model combining clinical, conventional, and fractal parameters was built using logistic regression analysis. Receiver operating characteristic curve analysis was used to assess the ability of the model to predict TERTp mutation status. RESULTS: Patients with TERTp-mutant grade 2 meningiomas were older (P = 0.029) and had higher 3D FD (P = 0.026) and lacunarity (P = 0.004) values than patients with TERTp-wildtype grade 2 meningiomas. On multivariable logistic analysis, higher 3D FD values (odds ratio = 32.50, P = 0.039) and higher 3D lacunarity values (odds ratio = 20.54, P = 0.014) were significant predictors of TERTp mutation status. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.84 (95% confidence interval 0.71-0.93), 83.3%, 71.4%, and 85.4%, respectively. CONCLUSION: 3D FD and lacunarity may be useful imaging biomarkers for predicting TERTp mutation status in grade 2 meningiomas.

Sex as a prognostic factor in adult-type diffuse gliomas: an integrated clinical and molecular analysis according to the 2021 WHO classification.

PURPOSE: To investigate whether type-specific sex differences in survival exist independently of clinical and molecular factors in adult-type diffuse gliomas according to the 2021 World Health Organization (WHO) classification. METHODS: A retrospective chart and imaging review of 1325 patients (mean age, 54 ± 15 years; 569 females) with adult-type diffuse gliomas (oligodendroglioma, IDH-mutant, and 1p/19q-codeleted, n = 183; astrocytoma, IDH-mutant, n = 211; glioblastoma, IDH-wildtype, n = 800; IDH-wildtype diffuse glioma, NOS, n = 131) was performed. The demographic information, extent of resection, imaging data, and molecular data including O6-methylguanine-methyltransferase promoter methylation (MGMT) promotor methylation were collected. Sex differences in survival were analyzed using Cox analysis. RESULTS: In patients with glioblastoma, IDH-wildtype, female sex remained as an independent predictor of better overall survival (hazard ratio = 0.91, P = 0.031), along with age, histological grade 4, MGMT promoter methylation status, and gross total resection. Female sex showed a higher prevalence of MGMT promoter methylation (40.2% vs 32.0%, P = 0.017) but there was no interaction effect between female sex and MGMT promoter methylation status (P-interaction = 0.194), indicating independent role of female sex. The median OS for females were 19.2 months (12.3-35.0) and 16.2 months (10.5-30.6) for males. No sex difference in survival was seen in other types of adult-type diffuse gliomas. CONCLUSION: There was a female survival advantage in glioblastoma, IDH-wildtype, independently of clinical data or MGMT promoter methylation status. There was no sex difference in survival in other types of adult-type diffuse gliomas, suggesting type-specific sex effects solely in glioblastoma, IDH-wildtype.

Salvage multiple burr hole surgery in patients with Moyamoya disease: efficacy evaluation using probabilistic independent component analysis of dynamic susceptibility contrast perfusion MRI.

PURPOSE: Multiple burr hole surgery is considered to be an option for achieving indirect revascularization in patients with ischemic Moyamoya disease (MMD). We aimed to investigate the efficacy of stand-alone multiple burr hole surgery for salvage revascularization in patients with MMD by assessing the hemodynamic changes via normalized time-to-peak (nTTP) analysis and independent component analysis (ICA) of preoperative and postoperative dynamic susceptibility contrast (DSC) perfusion MRI data. METHODS: The DSC perfusion MRI data of 25 hemispheres from 21 patients with MMD, who underwent multiple burr hole surgery for salvage revascularization due to persistent or recurrent symptoms after primary revascularization with modified encephaloduroarteriosynangiosis (mEDAS), were analyzed. The nTTP, which was measured using the region of interests covering the entire surgical hemisphere, was compared between the preoperative and postoperative images. ICA was used to compare the relative arterial and venous components of the surgical hemispheres between the respective preoperative and postoperative images. RESULTS: The median postoperative nTTP (1.80 s) was significantly shorter than the median preoperative nTTP (4.10 s) (P < 0.001). The postoperative relative arterial component of the surgical hemisphere (median: 0.04) was significantly higher than the preoperative relative arterial component (median: - 0.02, P < 0.001). In contrast, the postoperative relative venous component of the surgical hemisphere (median: - 0.05) was significantly lower than the preoperative value (median: 0.05, P < 0.001). CONCLUSION: The improvement in cerebral perfusion parameters observed on postoperative DSC perfusion MRI demonstrated that stand-alone multiple burr hole surgery could be a favorable salvage revascularization technique after mEDAS failure in patients with ischemic MMD.

Clinical factors and conventional MRI may independently predict progression-free survival and overall survival in adult pilocytic astrocytomas.

PURPOSE: Pilocytic astrocytoma (PA) is rare in adults, and only limited knowledge on the clinical course and prognosis has been available. The combination of clinical information and comprehensive imaging parameters could be used for accurate prognostic stratification in adult PA patients. This study was conducted to predict the prognostic factors from clinical information and conventional magnetic resonance imaging (MRI) features in adult PAs. METHODS: A total of 56 adult PA patients were enrolled in the institutional cohort. Clinical characteristics including age, sex, anaplastic PA, presence of neurofibromatosis type 1, Karnofsky performance status, extent of resection, and postoperative treatment were collected. MRI characteristics including major axis length, tumor location, presence of the typical 'cystic mass with enhancing mural nodule appearance', proportion of enhancing tumor, the proportion of edema, conspicuity of the nonenhancing margin, and presence of a cyst were evaluated. Univariable and multivariable Cox proportional hazard modeling were performed. RESULTS: The 5-year progression-free survival (PFS) and overall survival (OS) rates were 83.9% and 91.l%, respectively. On univariable analysis, older age, larger proportion of edema, and poor definition of nonenhancing margin were predictors of shorter PFS and OS, respectively (all Ps < .05). On multivariable analysis, older age (hazard ratio [HR] = 1.04, P = .014; HR = 1.14, P = .030) and poor definition of nonenhancing margin (HR = 3.66, P = .027; HR = 24.30, P = .024) were independent variables for shorter PFS and OS, respectively. CONCLUSION: Age and the margin of the nonenhancing part of the tumor may be useful biomarkers for predicting the outcome in adult PAs.

The feasibility of performing multiple burr hole surgery in pediatric moyamoya patients as a response to failed mEDAS.

OBJECTIVE: To investigate the feasibility and clinical effectiveness of performing multiple burr hole surgery in pediatric moyamoya patients as a response to failed modified encephaloduroarteriosynangiosis (mEDAS). METHODS: From January 2014 to May 2018, multiple burr hole surgery (MBS) was conducted on 16 hemispheres in 12 patients as a secondary treatment following mEDAS. The male-to-female ratio was 1:2 and the average age at the time of mEDAS was 6 years old. The average patient age was 9 ± 3 years olds (range 7-17) at the time of MBS which occurred an average of 46 months after mEDAS. An average of 10 ± 1 holes (range 8-13) were made. Time-to-peak (TTP) magnetic resonance images (MRI) were taken along 20 axial cuts. Of these cuts, two consecutive cuts on the lateral ventricle were selected to calculate the average value of the region of interest (ROI). The value of the cerebellum was subtracted from the average value of two consecutive cuts. The ROI value was analyzed using a paired t test by SPSS 20 (SPSS Inc., Chicago, IL, USA). RESULTS: All 16 cases presented improvement of clinical symptoms as determined by ROI analysis of the TTP MRI images. The average ROI value was 5.03 ± 6.36 before MBS and - 15.54 ± 9.42 after MBS. The average change in the ROI value was - 20.58 ± 12.59. The ROI value decreased in all cases after MBS. Magnetic resonance angiography (MRA) also showed a positive effect on vascularization. CONCLUSION: In pediatric moyamoya patients, MBS is recommended as secondary option as a response to failed mEDAS. Its clinical effectiveness was shown by analyzing TTP images and assisted by MRA and digital subtraction angiography.

Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.

OBJECTIVES: To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status. STUDY DESIGN: Retrospective cohort study. METHODS: Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status. CONCLUSIONS: Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E851-E856, 2021.

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics. METHODS: We reviewed 1166 preoperative MR images of gliomas (grades II-IV) from Severance Hospital (n = 856), Seoul National University Hospital (SNUH; n = 107), and The Cancer Imaging Archive (TCIA; n = 203). The Severance set was subdivided into the development (n = 727) and internal test (n = 129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2D tumor images and radiomic features from 3D tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets. RESULTS: The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86-0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%, with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86 and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively. CONCLUSIONS: Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

Mouse-human co-clinical trials demonstrate superior anti-tumour effects of buparlisib (BKM120) and cetuximab combination in squamous cell carcinoma of head and neck.

BACKGROUND: Recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN) is a common cancer with high recurrence and mortality. Current treatments have low response rates (RRs). METHODS: Fifty-three patients with R/M SCCHN received continuous oral buparlisib. In parallel, patient-derived xenografts (PDXs) were established in mice to evaluate resistance mechanisms and efficacy of buparlisib/cetuximab combination. Baseline and on-treatment tumour genomes and transcriptomes were sequenced. Based on the integrated clinical and PDX data, 11 patients with progression under buparlisib monotherapy were treated with a combination of buparlisib and cetuximab. RESULTS: For buparlisib monotherapy, disease control rate (DCR) was 49%, RR was 3% and median progression-free survival (PFS) and overall survival (OS) were 63 and 143 days, respectively. For combination therapy, DCR was 91%, RR was 18% and median PFS and OS were 111 and 206 days, respectively. Four PDX models were originated from patients enrolled in the current clinical trial. While buparlisib alone did not inhibit tumour growth, combination therapy achieved tumour inhibition in three of seven PDXs. Genes associated with apoptosis and cell-cycle arrest were expressed at higher levels with combination treatment than with buparlisib or cetuximab alone. CONCLUSIONS: The buparlisib/cetuximab combination has significant promise as a treatment strategy for R/M SCCHN. CLINICAL TRIAL REGISTRATION: NCT01527877.

Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach.

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613-0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467-0.759) and 0.663 (95% CI, 0.531-0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.