A radiogenomic clinical decision support system to inform individualized treatment in advanced nasopharyngeal carcinoma.

Both concurrent chemoradiotherapy (CCRT) and induction chemotherapy (ICT) followed by CCRT are standard care of advanced nasopharyngeal carcinoma (NPC). However, tailoring personalized treatment is lacking. Herein, we established a radiogenomic clinical decision support system to classify patients into three subgroups according to their predicted disease-free survival (DFS) with CCRT and ICT response. The CCRT-preferred group was suitable for CCRT since they achieved good survival with CCRT, which could not be improved by ICT. The ICT-preferred group was suitable for ICT plus CCRT since they had poor survival with CCRT; additional ICT could afford an improved DFS. The clinical trial-preferred group was suitable for clinical trials since they exhibited poor survival regardless of receiving CCRT or ICT plus CCRT. These findings suggest that our radiogenomic clinical decision support system could identify optimal candidates for CCRT, ICT plus CCRT, and clinical trials, and may thus aid in personalized management of advanced NPC.

A magnetic resonance imaging-based lymph node regression grading scheme for nasopharyngeal carcinoma after radiotherapy.

Background: Among patients with nasopharyngeal carcinoma (NPC), there is no established method to distinguish between patients with residual disease that may eventually progress and those who have achieved cured. We thus aimed to assess the prognostic value of magnetic resonance imaging (MRI)-based lymph node regression grade (LRG) in the risk stratification of patients with NPC following radiotherapy (RT). Methods: This study retrospectively enrolled 387 patients newly diagnosed with NPC between January 2010 and January 2013. A four-category MRI-LRG system based on the areal analysis of RT-induced fibrosis and residual tumor was established. Univariate analysis was performed using the Kaplan-Meier method, and comparisons were conducted via the log-rank test. Multivariate analyses were conducted using Cox regression models to calculate the hazard ratios (HRs) with 95% confidence intervals (CIs) and adjusted P values. Survival curves were calculated using the Kaplan-Meier method and compared using the log-rank test. Results: The sum of MRI-LRG scores (LRG-sum) was an independent prognostic factor for progression-free survival (PFS) (HR 2.50, 95% CI: 1.28-4.90; P<0.001). LRG-sum ≤9 and >9 showed a poorer 5-year PFS rate than did LRG-sum ≤2 (66.1%, 42.9%, and 77.6%, respectively; P<0.001). A survival clustering analysis-based decision tree model showed more complex interactions among LRG-sum and pretreatment and post-RT Epstein-Barr virus (EBV) DNA, yielding four patient clusters with differentiated disease progression risks (5-year PFS rates of 89.5%, 76.4%, 57.6%, and 27.8%, respectively), which showed better risk stratification than did post-RT EBV DNA alone (P<0.001). Conclusions: The MRI-LRG system adds prognostic information and is a potentially reliable, noninvasive means to stratify treatment modalities for patients with NPC.

Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports.

BACKGROUND: Development of distant metastasis (DM) is a major concern during treatment of nasopharyngeal carcinoma (NPC). However, studies have demonstrated improved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy. Therefore, precise prediction of metastasis in patients with NPC is crucial. AIM: To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging (MRI) reports. METHODS: This retrospective study included 792 patients with non-distant metastatic NPC. A total of 469 imaging variables were obtained from detailed MRI reports. Data were stratified and randomly split into training (50%) and testing sets. Gradient boosting tree (GBT) models were built and used to select variables for predicting DM. A full model comprising all variables and a reduced model with the top-five variables were built. Model performance was assessed by area under the curve (AUC). RESULTS: Among the 792 patients, 94 developed DM during follow-up. The number of metastatic cervical nodes (30.9%), tumor invasion in the posterior half of the nasal cavity (9.7%), two sides of the pharyngeal recess (6.2%), tubal torus (3.3%), and single side of the parapharyngeal space (2.7%) were the top-five contributors for predicting DM, based on their relative importance in GBT models. The testing AUC of the full model was 0.75 (95% confidence interval [CI]: 0.69-0.82). The testing AUC of the reduced model was 0.75 (95%CI: 0.68-0.82). For the whole dataset, the full (AUC = 0.76, 95%CI: 0.72-0.82) and reduced models (AUC = 0.76, 95%CI: 0.71-0.81) outperformed the tumor node-staging system (AUC = 0.67, 95%CI: 0.61-0.73). CONCLUSION: The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC. The number of metastatic cervical nodes was identified as the principal contributing variable.

Modulation of diverse biological processes by CPSF, the master regulator of mRNA 3' ends.

The cleavage and polyadenylation specificity factor (CPSF) complex plays a central role in the formation of mRNA 3' ends, being responsible for the recognition of the poly(A) signal sequence, the endonucleolytic cleavage step, and recruitment of poly(A) polymerase. CPSF has been extensively studied for over three decades, and its functions and those of its individual subunits are becoming increasingly well-defined, with much current research focusing on the impact of these proteins on the normal functioning or disease/stress states of cells. In this review, we provide an overview of the general functions of CPSF and its subunits, followed by a discussion of how they exert their functions in a surprisingly diverse variety of biological processes and cellular conditions. These include transcription termination, small RNA processing, and R-loop prevention/resolution, as well as more generally cancer, differentiation/development, and infection/immunity.

A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma.

BACKGROUND: The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. METHODS: We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). RESULTS: In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. CONCLUSIONS: The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.

Induction-concurrent chemoradiotherapy with or without sintilimab in patients with locoregionally advanced nasopharyngeal carcinoma in China (CONTINUUM): a multicentre, open-label, parallel-group, randomised, controlled, phase 3 trial.

BACKGROUND: Anti-PD-1 therapy and chemotherapy is a recommended first-line treatment for recurrent or metastatic nasopharyngeal carcinoma, but the role of PD-1 blockade remains unknown in patients with locoregionally advanced nasopharyngeal carcinoma. We assessed the addition of sintilimab, a PD-1 inhibitor, to standard chemoradiotherapy in this patient population. METHODS: This multicentre, open-label, parallel-group, randomised, controlled, phase 3 trial was conducted at nine hospitals in China. Adults aged 18-65 years with newly diagnosed high-risk non-metastatic stage III-IVa locoregionally advanced nasopharyngeal carcinoma (excluding T3-4N0 and T3N1) were eligible. Patients were randomly assigned (1:1) using blocks of four to receive gemcitabine and cisplatin induction chemotherapy followed by concurrent cisplatin radiotherapy (standard therapy group) or standard therapy with 200 mg sintilimab intravenously once every 3 weeks for 12 cycles (comprising three induction, three concurrent, and six adjuvant cycles to radiotherapy; sintilimab group). The primary endpoint was event-free survival from randomisation to disease recurrence (locoregional or distant) or death from any cause in the intention-to-treat population. Secondary endpoints included adverse events. This trial is registered with ClinicalTrials.gov (NCT03700476) and is now completed; follow-up is ongoing. FINDINGS: Between Dec 21, 2018, and March 31, 2020, 425 patients were enrolled and randomly assigned to the sintilimab (n=210) or standard therapy groups (n=215). At median follow-up of 41·9 months (IQR 38·0-44·8; 389 alive at primary data cutoff [Feb 28, 2023] and 366 [94%] had at least 36 months of follow-up), event-free survival was higher in the sintilimab group compared with the standard therapy group (36-month rates 86% [95% CI 81-90] vs 76% [70-81]; stratified hazard ratio 0·59 [0·38-0·92]; p=0·019). Grade 3-4 adverse events occurred in 155 (74%) in the sintilimab group versus 140 (65%) in the standard therapy group, with the most common being stomatitis (68 [33%] vs 64 [30%]), leukopenia (54 [26%] vs 48 [22%]), and neutropenia (50 [24%] vs 46 [21%]). Two (1%) patients died in the sintilimab group (both considered to be immune-related) and one (<1%) in the standard therapy group. Grade 3-4 immune-related adverse events occurred in 20 (10%) patients in the sintilimab group. INTERPRETATION: Addition of sintilimab to chemoradiotherapy improved event-free survival, albeit with higher but manageable adverse events. Longer follow-up is necessary to determine whether this regimen can be considered as the standard of care for patients with high-risk locoregionally advanced nasopharyngeal carcinoma. FUNDING: National Natural Science Foundation of China, Key-Area Research and Development Program of Guangdong Province, Natural Science Foundation of Guangdong Province, Overseas Expertise Introduction Project for Discipline Innovation, Guangzhou Municipal Health Commission, and Cancer Innovative Research Program of Sun Yat-sen University Cancer Center. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma.

Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760-0.763]), providing a new tool for treatment planning in NPC.

Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Non-canonical isoforms of the mRNA polyadenylation factor WDR33 regulate STING-mediated immune responses.

The human WDR33 gene encodes three major isoforms. The canonical isoform WDR33v1 (V1) is a well-characterized nuclear mRNA polyadenylation factor, while the other two, WDR33v2 (V2) and WDR33v3 (V3), have not been studied. Here, we report that V2 and V3 are generated by alternative polyadenylation, and neither protein contains all seven WD (tryptophan-aspartic acid) repeats that characterize V1. Surprisingly, V2 and V3 are not polyadenylation factors but localize to the endoplasmic reticulum and interact with stimulator of interferon genes (STING), the immune factor that induces the cellular response to cytosolic double-stranded DNA. V2 suppresses interferon-ß induction by preventing STING disulfide oligomerization but promotes autophagy, likely by recruiting WIPI2 isoforms. V3, on the other hand, functions to increase STING protein levels. Our study has not only provided mechanistic insights into STING regulation but also revealed that protein isoforms can be functionally completely unrelated, indicating that alternative mRNA processing is a more powerful mechanism than previously appreciated.

Radiotherapy alone versus concurrent chemoradiotherapy in patients with stage II and T3N0 nasopharyngeal carcinoma with adverse features: A propensity score-matched cohort study.

BACKGROUND AND PURPOSE: Whether concurrent chemoradiotherapy would provide survival benefits in patients with stage II and T3N0 NPC with adverse factors remains unclear in IMRT era. We aimed to assess the value of concurrent chemotherapy compared to IMRT alone in stage II and T3N0 NPC with adverse features. MATERIALS AND METHODS: 287 patients with stage II and T3N0 NPC with adverse factors were retrospectively analyzed, including 98 patients who received IMRT alone (IMRT alone group) and 189 patients who received cisplatin-based concurrent chemotherapy (CCRT group). The possible prognostic factors were balanced using propensity score matching (PSM). Kaplan-Meier analysis was used to evaluate the survival rates, and log-rank tests were employed to compare differences between groups. RESULTS: The median follow-up duration was 90.8 months (interquartile range = 75.6-114.7 months). The IMRT alone and the CCRT group were well matched; however, for all survival-related endpoints, there were no significant differences between them (5-year failure-free survival: 84.3% vs. 82.7%, P value = 0.68; 5-year overall survival: 87.3% vs. 90.6%, P value = 0.11; 5-year distant metastasis-free survival: 92.8% vs. 92.5%, P value = 0.97; 5-year locoregional relapse-free survival: 93.4% vs. 89.9%, P value = 0.30). The incidence of acute toxicities in the IMRT alone group was significantly lower than that in the CCRT group. CONCLUSION: For patients with stage II and T3N0 NPC with adverse features treated using IMRT, no improvement in survival was gained by adding concurrent chemotherapy; however, the occurrence of acute toxicities increased significantly. For those combined with non-single adverse factors, the comprehensive treatment strategy needs further exploration.

Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma.

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.

Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy.

OBJECTIVES: This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC). MATERIALS AND METHODS: A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods. RESULTS: The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients. CONCLUSION: The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT. CLINICAL RELEVANCE STATEMENT: The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment. KEY POINTS: • The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.

Optimal cut-off value for identifying objective response in patients with nasopharyngeal carcinoma after induction chemotherapy.

BACKGROUND: We aimed to establish the most suitable threshold for objective response (OR) in the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 in patients with nasopharyngeal carcinoma (NPC). METHODS: According to RECIST 1.1, we retrospectively evaluated MR images of NPC lesions in patients before and after induction chemotherapy (IC). Restricted cubic spline and maximally selected rank statistics were used to determine the cut-off value. Survival rates and differences between groups were compared with Kaplan-Meier curves and log-rank tests. RESULTS: Of 1126 patients, 365 cases who received IC treatment were suitable for RECIST 1.1 evaluation. The 20% cut-off value maximized between-group differences according to maximally selected rank statistics. No difference in distant metastasis-free survival between OR and non-response groups was shown using the primary threshold of OR (30%), while it differed when 20% was employed. CONCLUSIONS: With an optimal cut-off value of 20%, RECIST may assist clinicians to accurately evaluate disease response in NPC patients.

In Situ Study and Improvement of the Temperature Increase and Isothermal Retention Stages in the Polyacrylonitrile (PAN) Fiber Pre-Oxidation Process.

The pre-oxidation process of Polyacrylonitrile (PAN) fibers is a complex procedure involving multiple stages of temperature increase and isothermal temperature retention. However, the impact of the temperature increase stage on PAN fiber has often been overlooked. To address this, samples were collected before and after the temperature increase and isothermal retention stages, treating them as separate influencing factors. Therefore, the pre-oxidation process can be divided into four distinct stages: (1) A temperature increase stage before the cyclization reactions: the PAN fiber's small-size crystals melt, and the crystal orientation changes under fixed tension, leading to shrinkage and increased orientation of the micropore. (2) An isothermal retention stage before the cyclization reactions: The crystal structure maintains well, resulting in minimal micropore evolution. The PAN fiber's crystal orientation and micropore orientation increased under fixed tension. (3) A temperature increase stage after the cyclization reactions: The PAN fiber's crystal melts again, reducing the average chord length and relative volume of the micropore. However, the PAN fiber can recrystallize under fixed tension. (4) An isothermal retention stage after the cyclization reactions: Significant crystal melting of the PAN fiber occurs, but the highly oriented crystals are maintained well. The average chord length and relative volume of the micropore increase. Recommendations for improving the pre-oxidation process are made according to these stages.

TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images.

Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer and CNN in an end-to-end structure to capture both the short- and long-range dependency to provide rich information on the characteristics of nodules. Second, we design the attention block and multi-scale skip pathways for improving the detection of small nodules. Last, we develop a two-head detector to guarantee high sensitivity and specificity. Experimental results on the LUNA16 dataset and PN9 dataset showed that our proposed TiCNet achieved superior performance compared with existing lung nodule detection methods. Moreover, the effectiveness of each module has been proven. The proposed TiCNet model is an effective tool for pulmonary nodule detection. Validation revealed that this model exhibited excellent performance, suggesting its potential usefulness to support lung cancer screening.

Value of radiological depth of invasion in non-pT4 Oral tongue squamous cell carcinoma: implication for preoperative MR T-staging.

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.

TdDS-UNet: top-down deeply supervised U-Net for the delineation of 3D colorectal cancer.

Automatically delineating colorectal cancers with fuzzy boundaries from 3D images is a challenging task, but the problem of fuzzy boundary delineation in existing deep learning-based methods have not been investigated in depth. Here, an encoder-decoder-based U-shaped network (U-Net) based on top-down deep supervision (TdDS) was designed to accurately and automatically delineate the fuzzy boundaries of colorectal cancer. TdDS refines the semantic targets of the upper and lower stages by mapping ground truths that are more consistent with the stage properties than upsampling deep supervision. This stage-specific approach can guide the model to learn a coarse-to-fine delineation process and improve the delineation accuracy of fuzzy boundaries by gradually shrinking the boundaries. Experimental results showed that TdDS is more customizable and plays a role similar to the attentional mechanism, and it can further improve the capability of the model to delineate colorectal cancer contours. A total of 103, 12, and 29 3D pelvic magnetic resonance imaging volumes were used for training, validation, and testing, respectively. The comparative results indicate that the proposed method exhibits the best comprehensive performance, with a dice similarity coefficient (DSC) of 0.805 ± 0.053 and a hausdorff distance (HD) of 9.28 ± 5.14 voxels. In the delineation performance analysis section also showed that 44.49% of the delineation results are satisfactory and do not require revisions. This study can provide new technical support for the delineation of 3D colorectal cancer. Our method is open source, and the code is available athttps://github.com/odindis/TdDS/tree/main.

LD-UNet: A long-distance perceptual model for segmentation of blurred boundaries in medical images.

The blurriness of boundaries in medical image target regions hinders further improvement in automatic segmentation accuracy and is a challenging problem. To address this issue, we propose a model called long-distance perceptual UNet (LD-UNet), which has a powerful long-|distance perception ability and can effectively perceive the semantic context of an entire image. Specifically, LD-UNet utilizes global and local long-distance induction modules, which endow the model with contextual semantic induction capabilities for long-distance feature dependencies. The modules perform long-distance semantic perception at the high and low stages of LD-UNet, respectively, effectively improving the accuracy of local blurred information assessment. We also propose a top-down deep supervision method to enhance the ability of the model to fit data. Then, extensive experiments on four types of tumor data with blurred boundaries are conducted. The dataset includes nasopharyngeal carcinoma, esophageal carcinoma, pancreatic carcinoma, and colorectal carcinoma. The dice similarity coefficient scores obtained by LD-UNet on the four datasets are 73.35%, 85.93%, 70.04%, and 82.71%. Experimental results demonstrate that LD-UNet is more effective in improving the segmentation accuracy of blurred boundary regions than other methods with long-distance perception, such as transformers. Among all models, LD-UNet achieves the best performance. By visualizing the feature dependency field of the models, we further explore the advantages of LD-UNet in segmenting blurred boundaries.

Feasible CT features to distinguish incidental rib enhancement from sclerotic metastasis in patients with malignancies.

OBJECTIVE: To investigate the CT features of incidental rib enhancement (RE) and to summarize the CT characteristics for distinguishing the RE from sclerotic metastasis (SM) in patients with malignancies. MATERIAL AND METHODS: This retrospective observational study enrolled 79 patients with RE (involved 133 ribs) during October 2014 and December 2021. Another 53 patients with SM (160 SM) in the same period were selected randomly for comparison. The location, enhancement patterns of RE were reviewed. The CT values of RE regions and SM were measured and statistically analyzed. RESULTS: Most REs (70 patients, 88.6%) were in the 1st to 6th ribs. 50 patients had solitary RE and 29 with multiple REs in a regional distribution. All the REs were closely connected to the intercostal venous plexus (ICVP) ipsilateral to the injection site. No visible abnormalities on unenhanced scans were detected in all REs. One hundred and twenty REs (90.2%) had nodular/patchy enhancement. The CT value of RE regions in the venous phase was lower than that in the arterial phase (589.8 ± 344.2 HU versus 1188.5 ± 325.3 HU, p < 0.001). During the venous phase, most REs (125, 94.0%) shrank or disappeared. SM appeared similar on both contrast-enhanced and unenhanced scans in terms of shape and CT values. CONCLUSION: The RE demonstrated characteristic CT features. The manifestations of nodular/patchy enhancement in the arterial phase, decreased density and shrinkage or disappearance during the venous phase, and no abnormality on unenhanced scans, as well as a close connection with the ICVP, may help differentiate RE from SM.

Refining the 8th edition TNM classification for EBV related nasopharyngeal carcinoma.

The AJCC/UICC TNM classification describes anatomic extent of tumor progression and guides treatment decisions. Our comprehensive analysis of 8,834 newly diagnosed patients with non-metastatic Epstein-Barr virus related nasopharyngeal carcinoma (NPC) from six Chinese centers indicates certain limitations in the current staging system. The 8th edition of the AJCC/UICC TNM classification inadequately differentiates patient outcomes, particularly between T2 and T3 categories and within the N classification. We propose reclassifying cases of T3 NPC with early skull-base invasion as T2, and elevating N1-N2 cases with grade 3 image-identified extranodal extension (ENE) to N3. Additionally, we suggest combining T2N0 with T1N0 into a single stage IA. For de novo metastatic (M1) NPC, we propose subdivisions of M1a, defined by 1-3 metastatic lesions without liver involvement, and M1b, characterized by >3 metastatic lesions or liver involvement. This proposal better reflects responses of NPC patients to the up-to-date treatments and their evolving risk profiles.