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Objective: The prognostic stratification for oral tongue squamous cell carcinoma (OTSCC) is heavily based on postoperative pathological depth of invasion (pDOI). This study aims to propose a preoperative MR T-staging system based on tumor size for non-pT4 OTSCC. Methods: Retrospectively, 280 patients with biopsy-confirmed, non-metastatic, pT1-3 OTSCC, treated between January 2010 and December 2017, were evaluated. Multiple MR sequences, including axial T2-weighted imaging (WI), unenhanced T1WI, and axial, fat-suppressed coronal, and sagittal contrast-enhanced (CE) T1WI, were utilized to measure radiological depth of invasion (rDOI), tumor thickness, and largest diameter. Intra-class correlation (ICC) and univariate and multivariate analyses were used to evaluate measurement reproducibility, and factors' significance, respectively. Cutoff values were established using an exhaustive method. Results: Intra-observer (ICC = 0.81–0.94) and inter-observer (ICC = 0.79–0.90) reliability were excellent for rDOI measurements, and all measurements were significantly associated with overall survival (OS) (all p <.001). Measuring the rDOI on axial CE-T1WI with cutoffs of 8 mm and 12 mm yielded an optimal MR T-staging system for rT1-3 disease (5-year OS of rT1 vs rT2 vs rT3: 94.0% vs 72.8% vs 57.5%). Using multivariate analyses, the proposed T-staging exhibited increasingly worse OS (hazard ratio of rT2 and rT3 versus rT1, 3.56 [1.35–9.6], p =.011; 4.33 [1.59–11.74], p =.004; respectively), which outperformed pathological T-staging based on nonoverlapping Kaplan–Meier curves and improved C-index (0.682 vs. 0.639, p <.001). Conclusions: rDOI is a critical predictor of OTSCC mortality and facilitates preoperative prognostic stratification, which should be considered in future oral subsite MR T-staging. Clinical relevance statement: Utilizing axial CE-T1WI, an MR T-staging system for non-pT4 OTSCC was developed by employing rDOI measurement with optimal thresholds of 8 mm and 12 mm, which is comparable with pathological staging and merits consideration in future preoperative oral subsite planning. Key Points: • Tumor morphology, measuring sequences, and observers could impact MR-derived measurements and compromise the consistency with histology. • MR-derived measurements, including radiological depth of invasion (rDOI), tumor thickness, and largest diameter, have a prognostic impact on OS (all p <.001). • rDOI with cutoffs of 8 mm and 12 mm on axial CE-T1WI is an optimal predictor of OS and could facilitate risk stratification in non-pT4 OTSCC disease. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the prognostic significance of MR-detected mandibular nerve involvement (MNI) and its value for induction chemotherapy (IC) administration in patients with nasopharyngeal carcinoma (NPC) and T4 disease. Methods: This retrospective study enrolled 792 non-metastatic, biopsy-proven NPC patients. Univariate and multivariate analysis were used to evaluate potential prognosticators. The inter-observer agreement was assessed by the kappa values. Results: MR-detected MNI was observed in 141 (72.3%) patients among 195 patients with T4 disease, with excellent agreement between the readers (kappa = 0.926). Patients with MR-detected MNI presented better 5-year overall survival (OS) (hazard ratio [HR], 0.40; P = 0.006) than those with MR-negative MNI. Of these patients, IC treatment was verified as an independent factor (HR: 0.35; P = 0.014) with preferable effect on OS. Conclusion: MR-detected MNI could serve as an independent favorable prognostic predictor for OS in NPC patients with stage T4, which should be considered for stratifying these patients for IC administration. [ABSTRACT FROM AUTHOR]

Objectives: Prognoses for nasopharyngeal carcinoma (NPC) between categories T2 and T3 in the Eighth American Joint Committee on Cancer (AJCC) staging system were overlapped. We explored the value of skull base invasion (SBI) subclassification in prognostic stratification and use of induction chemotherapy (IC) to optimize T2/T3 categorization for NPC patients. Methods: We retrospectively reviewed 1752 NPC patients from two hospitals. Eight skull base bone structures were evaluated. Survival differences were compared between slight SBI (T3 patients with pterygoid process and/or base of the sphenoid bone invasion only) and severe SBI (T3 patients with other SBIs) with or without IC using random matched-pair analysis. We calculated the prognosis and Harrel concordance index (C-index) for the revised T category and compared IC outcomes for the revised tumor stages. Results: Compared to severe SBI, slight SBI showed better 5-year overall survival (OS) (81.5% vs. 92.3%, p = 0.001) and progression-free survival (PFS) (71.5% vs. 83.0%, p = 0.002). Additional IC therapy did not significantly improve OS and PFS in slight SBI. The proposed T category separated OS, PFS, and locoregional recurrence-free survival in T2 and T3 categories with statistical significance. An improved C-index for OS prediction was observed in the proposed T category with combined confounding factors, compared to the AJCC T staging system (0.725 vs. 0.713, p = 0.046). The survival benefits of IC were more obvious in the advanced stage. Conclusions: NPC patients with slight SBI were recommended to downstage to T2 category. The adjustment for T category enabled better prognostic stratification and guidance for IC use. Key Points: • For nasopharyngeal carcinoma (NPC) patients in T3 category, slight skull base invasion was a significant positive predictor for OS and PFS. • NPC patients with slight SBI might not gain significant survival benefits from induction chemotherapy. • Downstaging slight SBI NPC patients to T2 category would make a more accurate risk stratification, improve the predicting performance in OS, and have a better guidance in the use of IC for patients in advanced stage. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to assess the prognostic value of quantitative cervical nodal necrosis (CNN) burden in N staging risk stratification in patients with nasopharyngeal carcinoma. Methods: Univariate and multivariate Cox regression models evaluated the association between lymph node variables based on MRI images and survival. Revisions for the N classification system were proposed and compared to the 8th edition AJCC staging system using Harrell's concordance index (C-index). The survival outcomes of induction chemotherapy plus concurrent chemoradiotherapy (CCRT) and CCRT alone in patients with multiple CNNs were compared. Results: In 1319 patients enrolled, CNN was not an independent prognostic factor for the main survival outcomes, but multiple CNNs (three or more necrotic nodes) were independent prognostic factors for distant metastasis-free survival (DMFS) (adjusted hazard ratio [HR], 2.05; p = 0.020) and progression-free survival (PFS) (HR, 1.78; p = 0.004), surpassing other nodal variables. On upgrading patients with multiple CNNs to revised N3 disease, the proposed N staging widened the differences in DMFS and PFS between N2 and N3 disease. The overall survival of patients with multiple CNNs who received CCRT plus induction chemotherapy was improved compared to that of those who received CCRT alone (76.1% vs. 55.7%; adjusted p = 0.030). Conclusions: Upgrading patients with multiple CNNs to stage N3 may improve prognostication of the current AJCC staging system. Multiple CNNs might be a potential marker for stratifying patients who would benefit from induction chemotherapy. Key Points: • Quantitatively assessed the prognostic value of CNN burden in patients with NPC. • Upgrading patients with multiple CNNs to stage N3 may improve prognostication. • Multiple CNNs may be used as a stratification marker for induction chemotherapy. [ABSTRACT FROM AUTHOR]

Objectives: Develop and evaluate a deep learning-based automatic meningioma segmentation method for preoperative meningioma differentiation using radiomic features.Methods: A retrospective multicentre inclusion of MR examinations (T1/T2-weighted and contrast-enhanced T1-weighted imaging) was conducted. Data from centre 1 were allocated to training (n = 307, age = 50.94 ± 11.51) and internal testing (n = 238, age = 50.70 ± 12.72) cohorts, and data from centre 2 external testing cohort (n = 64, age = 48.45 ± 13.59). A modified attention U-Net was trained for meningioma segmentation. Segmentation accuracy was evaluated by five quantitative metrics. The agreement between radiomic features from manual and automatic segmentations was assessed using intra class correlation coefficient (ICC). After univariate and minimum-redundancy-maximum-relevance feature selection, L1-regularized logistic regression models for differentiating between low-grade (I) and high-grade (II and III) meningiomas were separately constructed using manual and automatic segmentations; their performances were evaluated using ROC analysis.Results: Dice of meningioma segmentation for the internal testing cohort were 0.94 ± 0.04 and 0.91 ± 0.05 for tumour volumes in contrast-enhanced T1-weighted and T2-weighted images, respectively; those for the external testing cohort were 0.90 ± 0.07 and 0.88 ± 0.07. Features extracted using manual and automatic segmentations agreed well, for both the internal (ICC = 0.94, interquartile range: 0.88-0.97) and external (ICC = 0.90, interquartile range: 0.78-70.96) testing cohorts. AUC of radiomic model with automatic segmentation was comparable with that of the model with manual segmentation for both the internal (0.95 vs. 0.93, p = 0.176) and external (0.88 vs. 0.91, p = 0.419) testing cohorts.Conclusions: The developed deep learning-based segmentation method enables automatic and accurate extraction of meningioma from multiparametric MR images and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.Key Points: • A deep learning-based method was developed for automatic segmentation of meningioma from multiparametric MR images. • The automatic segmentation method enabled accurate extraction of meningiomas and yielded radiomic features that were highly consistent with those that were obtained using manual segmentation. • High-grade meningiomas were preoperatively differentiated from low-grade meningiomas using a radiomic model constructed on features from automatic segmentation. [ABSTRACT FROM AUTHOR]

Purpose: Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment. Methods: From August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40 keV was reconstructed from 100 and 150 keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40 keV, 100 keV, 150 keV, and 100 + 150 keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction. Results: DLR based on 100 + 150 keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85–0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91–0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012). Conclusions: The DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making. [ABSTRACT FROM AUTHOR]

Objectives: To identify the prognosis of parapharyngeal space involvement (PPSI) based on the number of subspaces involved (pre-styloid space, carotid space (CS), areas outside the CS) and explore its significance for current T-staging in patients with nasopharyngeal carcinoma (NPC). Methods: PPSI was retrospectively identified in 1224 patients with non-disseminated NPC at two centers on MRI and separated into four invasion patterns: pattern A (only post-styloid space), pattern B (post-styloid space, CS extension), pattern C (post-styloid space, pre-styloid space extension), and pattern D (all spaces). The Kaplan–Meier analysis and multivariate Cox regression models were used. Results: PPSI was diagnosed in 63.4% of cases, with patterns A, B, C, and D in 14.3%, 3.8%, 25.3%, and 18.6% of cases, respectively. No prognostic heterogeneity was observed between pattern B and pattern C (p > 0.05). Thus, the degree of PPSI was based on the number of subspaces involved: grade 0 (none), grade 1 (one), grade 2 (two), and grade 3 (three), which could independently predict overall survival (OS) (p < 0.001). T3 patients with grade 0/1 PPSI (slight-T3) had a better prognosis than those with grade 2/3 PPSI (severe-T3) in terms of OS, locoregional-free survival (LRFS), and progression-free survival (PFS) (all p < 0.001), whose hazard ratios were higher and lower than those with T1 and T2, respectively. Combining the T2 and slight-T3 groups as the proposed T2 provided significant differences in OS, LRFS, and PFS between T2 and T3 (all p < 0.05). Conclusions: The risk of death increased with the number of parapharyngeal subspaces involved. The degree of PPSI is recommended to optimize T3 heterogeneity. Key Points: • Parapharyngeal space involvement was proposed to differentiate patient risk groups based on the number of involved subspaces: grade 0 (none), grade 1 (one), grade 2 (two), or grade 3 (three). • The degree of parapharyngeal space involvement was an independent negative prognosticator for OS. • The degree of parapharyngeal space involvement may influence T-staging in patients with nasopharyngeal carcinoma. [ABSTRACT FROM AUTHOR]

Objective: To investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC)., Methods: This retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016-October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter., Results: Statistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915-0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967-0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant., Conclusion: Among all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model., (© 2024. The Author(s).)