Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study.

BACKGROUND: As part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the number of lymph node metastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC. OBJECTIVE: This study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning. METHODS: A total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classified into mild (stage T1 and T2), moderate (stage T3), and severe (stage T4) groups. Three predictive models based on the labeled CT images were constructed using the radiomics features (radiomics model), deep features (deep learning model), and a combination of both (hybrid model). RESULTS: The overall classification accuracy of the radiomics model was 64.3% in the internal testing data set. The deep learning model and hybrid model showed better performance than the radiomics model, with overall classification accuracies of 75.7% (P=.04) and 81.4% (P=.001), respectively. On the subtasks of binary classification of tumor severity, the areas under the curve of the radiomics, deep learning, and hybrid models were 0.875, 0.866, and 0.886 in the internal testing data set and 0.820, 0.818, and 0.972 in the external testing data set, respectively, for differentiating mild (stage T1~T2) from nonmild (stage T3~T4) patients, and were 0.815, 0.892, and 0.894 in the internal testing data set and 0.685, 0.808, and 0.897 in the external testing data set, respectively, for differentiating nonsevere (stage T1~T3) from severe (stage T4) patients. CONCLUSIONS: The hybrid model integrating radiomics features and deep features showed favorable performance in diagnosing the pathological stage of GC.

Assessment of Endoscopic Ultrasound for Predicting Preoperative T Staging in Patients With Gastric Adenocarcinoma.

BACKGROUND AND OBJECTIVES: Endoscopic ultrasound (EUS) is an imaging modality that can be applied to predict preoperative T staging. It is important for the patient to decide whether to receive endoscopic therapy, surgical intervention, or neoadjuvant therapy. The objectives of our study were to (1) identify if EUS could precisely predict T1-stage tumor, which is suitable for endoscopic treatment and (2) identify if EUS could precisely predict tumors more advanced than T3, which would mandate neoadjuvant therapy. METHODS: A retrospective study of patients who received gastrectomy was conducted from March 2017 to December 2021 at Taichung Veterans General Hospital. Those who received preoperative EUS, with final pathology showing gastric adenocarcinoma were included. Consistency of EUS prediction and pathology, accuracy, and parameters impacting accuracy were analyzed. RESULTS: The κ value was 0.6 if the T stage was not grouped, indicating moderate agreement. Overall accuracy was 52.8%. Overestimation and underestimation rates were 17.6% and 29.5%, respectively. The accuracy of T stages is highest in T1 (85.23%). The κ value of T1-stage was 0.67, and those of T2, T3, and T4 were below 0.5. Regarding parameters affecting accuracy, we found longitudinal portion, Borrmann type, ulcer presentation, early gastric cancer, and size of tumor could influence the accuracy. CONCLUSIONS: Our study showed that EUS was a good tool for precisely predicting T1-stage of gastric adenocarcinoma preoperatively. For this situation, endoscopic treatment would be enough. However, for predicting more advanced gastric adenocarcinoma, EUS should be combined with other modalities to achieve better accuracy.

Accuracy of Preoperative Magnet Resonance Imaging to Predict Pathologic T-Stage in Patients With Cervical Cancer.

BACKGROUND/AIM: The trend in today's surgical gynecological oncology is to provide equal oncological safety with less radical surgery. The SHAPE trial demonstrated the non-inferiority of a simple hysterectomy compared to a radical hysterectomy in low-risk cervical cancer. As a result, the accuracy of preoperative diagnostics has become increasingly important to avoid both under- and overtreatment. The aim of the study was to investigate the accuracy of MRI-based T-stage. PATIENTS AND METHODS: Forty-five patients who were surgically treated for an initial diagnosis of a primary cervical carcinoma at the University Hospital Cologne in the Department of Gynecology and Obstetrics between 2015 and 2021 were included in the study. All patients underwent MRI prior to their surgical treatment. RESULTS: In 44.4% of cases, the pathological tumor size in the surgical specimen was consistent with the preoperative tumor size determined by MRI. In 28.9% of the cases, MRI overestimated the final pathologic T-stage while in 26.7% of cases, MRI underestimated it. Furthermore, we were able to show that overall survival was significantly poorer (p<0.05) in patients whose preoperative MRI had underestimated the final T-stage in our study cohort. CONCLUSION: Preoperative MRI diagnostics alone are not reliable enough for accurate T-stage estimation. Multimodal diagnostic approaches are essential for accurate preoperative staging. Prospective trials are needed to evaluate preoperative staging strategies to optimize sizing accuracy.

Articulation in postoperative patients treated for early- and advanced-stage tongue cancer.

Tongue cancer is among the most prevalent head and neck cancers and is most commonly treated via surgery. We assessed the articulation differences in patients with early and advanced tongue cancer. The study included 26 postoperative tongue cancer patients. We evaluated articulation based on bilabial, labiodental, apical, posterior, laminal, apical front, and apical back speech sounds. The 26 postoperative tongue cancer patients (24 males, two females) had an average age of 54.3 (range 37-89 old) years. Twelve patients were classified as early T stage (T1/T2), while 14 were classified as advanced T stage (T3/T4). The total articulation score was not significantly related to the T stage but apical sounds were so associated. The total articulation score did not significantly differ between postoperative patients with early and advanced tongue cancer. In subgroup analysis, the apical sound was significantly impaired in postoperative patients with advanced tongue cancer.

Accuracy of Baseline Magnetic Resonance Imaging for Staging Rectal Cancer Patients Proceeding Directly to Surgery.

BACKGROUND AND OBJECTIVES: High-resolution magnetic resonance imaging (MRI) accuracy for staging preoperative rectal cancer varies across studies. We examined MRI accuracy for T- and N-staging of rectal cancer compared with final histopathology of the resected specimen in a large Australian cohort who did not receive neoadjuvant therapy or radiation. METHODS: Retrospective analysis of prospectively-collected clinical data from 153 rectal adenocarcinomas locally staged by high-resolution MRI between January 2012 and December 2019 that did not undergo chemoradiotherapy or radiation before surgery. T- and N-stage agreement between MRI and final histopathology was assessed using Kappa statistic. Agreement at each T-stage was evaluated using log-linear modeling. N-staging accuracy was examined using positive and negative predictive values. RESULTS: Overall agreement between MRI and final histopathology for T-stage and N-stage was 55% and 65%, respectively. Kappa statistic found higher agreement between MRI and final histopathology for T-staging (κ = 0.33) versus N-staging (κ = 0.18). MRI correctly assessed 91% of T1 tumors, 43% of T2 tumors, 65% of T3 tumors, and 80% of T4 tumors. MRI accuracy was higher for N-negative tumors (74.1%) than for N-positive tumors (44.4%). CONCLUSION: MRI is moderately accurate at staging T1, T3, and T4 rectal tumors but caution when staging tumors as T2 is advised. Greater accuracy for staging N-negative versus N-positive tumors is indicated.

Lymph node dissection does not affect the survival of patients with tumor node metastasis stages I and II colorectal cancer.

BACKGROUND: The effect of the number of lymph node dissections (LNDs) during radical resection for colorectal cancer (CRC) on overall survival (OS) remains controversial. AIM: To investigate the association between the number of LNDs and OS in patients with tumor node metastasis (TNM) stage I-II CRC undergoing radical resection. METHODS: Patients who underwent radical resection for CRC at a single-center hospital between January 2011 and December 2021 were retrospectively analyzed. Cox regression analyses were performed to identify the independent predictors of OS at different T stages. RESULTS: A total of 2850 patients who underwent laparoscopic radical resection for CRC were enrolled. At stage T1, age [P < 0.01, hazard ratio (HR) = 1.075, 95% confidence interval (CI): 1.019-1.134] and tumour size (P = 0.021, HR = 3.635, 95%CI: 1.210-10.917) were independent risk factors for OS. At stage T2, age (P < 0.01, HR = 1.064, 95%CI: 1.032-1.098) and overall complications (P = 0.012, HR = 2.297, 95%CI: 1.200-4.397) were independent risk factors for OS. At stage T3, only age (P < 0.01, HR = 1.047, 95%CI: 1.027-1.066) was an independent risk factor for OS. At stage T4, age (P < 0.01, HR = 1.057, 95%CI: 1.039-1.075) and body mass index (P = 0. 034, HR = 0.941, 95%CI: 0.890-0.995) were independent risk factors for OS. However, there was no association between LNDs and OS in stages I and II. CONCLUSION: The number of LDNs did not affect the survival of patients with TNM stages I and II CRC. Therefore, insufficient LNDs should not be a cause for alarm during the surgery.

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma.

Background: Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. Objectives: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. Materials and methods: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. Conclusions: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

The Incidence of Breast Cancer Lymph Node Involvement in Trinidad and Tobago.

Objective This study aims to investigate breast cancer lymph node involvement in a West Indian population while correlating it with various histological parameters and evaluating the role of the sentinel lymph node biopsy. Method This is a retrospective study where histology reports for all breast cancer-related biopsies from 2018 to 2021, totaling 813 samples, were obtained. Histological parameters from these reports were extracted into a spreadsheet and analyzed using Statistical Product and Service Solutions (SPSS, version 28.0; IBM SPSS Statistics for Windows, Armonk, NY) software for TNM staging and axillary and sentinel lymph node dissections, among other fields found in histology reports. Results In 44.8% of cases, patients present at the T2 stage with associated lymph node spread. Each T stage had more lymph nodes involved than uninvolved for tumors sized T2 and higher. Inversely, for tumors staged under T2, there were generally more uninvolved lymph nodes than involved ones. Larger tumors were found to have advanced N staging, especially in the T3 category, where a significantly higher proportion of cases were found to have N2 and N3 staging compared to the other T stages. This trend is also seen in M staging, where larger tumors metastasize more than smaller tumors (40% for T4a, 0% for T1). Despite significant lymph node involvement being observed, sentinel lymph node biopsies were usually negative. Conclusion More patients in this population present with lymph node involvement than without. Larger breast cancer tumors are associated with greater lymph node involvement, particularly at T2 and higher stages. Sentinel lymph node biopsies can be omitted in smaller breast cancer tumors up to 2 cm in size, and the local recurrence rate is low despite a false-negative rate of around 10% in upfront sentinel lymph node biopsy.

The role of histological subtype and chemotherapy on prognosis of ureteral cancer.

OBJECTIVE: To date, there have been few studies examining the prognostic implications of histological subtypes in ureteral cancer. And chemotherapy plays a crucial role in the treatment of ureteral cancer, while many factors influence the efficacy of chemotherapy. This study aimed to utilize the Surveillance, Epidemiology and End Results database to assess the impact of histological type on ureteral cancer prognostic outcomes and discovered how histological type and T-stage influence the efficacy of chemotherapy. METHODS: Based on Surveillance, Epidemiology, and End Results Program, we reviewed 8915 records of patients with primary ureteral cancer from 18 centers between 2000 and 2018. We focused on the overall survival and cancer-specific survival of the records and used Kaplan‒Meier method to calculate survival curves. RESULTS: In the comparison of prognostic outcomes, atypical subtypes exhibited a less favorable prognosis compared to typical ureteral carcinoma. Notably, patients diagnosed with papillary urothelial carcinoma demonstrated the most favorable overall survival (p = 0.005). Statistically significant benefits were observed in the prognosis of patients with non-papillary urothelial carcinoma who received chemotherapy (HR = 0.860, 95% CI 0.764-0.966, p = 0.011), while chemotherapy did not yield a statistically significant effect on the prognosis of patients with papillary urothelial carcinoma (HR = 1.055, 95% CI 0.906-1.228, p = 0.493). Chemotherapy had an adverse impact on the prognosis of patients with T1 ureteral cancer (HR = 1.235, 95% CI 1.016-1.502, p = 0.034), whereas it exhibited a positive prognostic effect for T3/T4 cases (HR = 0.739, 95% CI 0.654-0.835, p < 0.001). CONCLUSIONS: Histological type affects the prognosis of ureteral cancer. And evaluation of cancer histological type and T stage in ureteral cancer patients prior to chemotherapy is mandatory.

Diagnostic efficacy of contrast-enhanced gastric ultrasonography in staging gastric cancer: a meta-analysis.

BACKGROUND: As comprehensive surgical management for gastric cancer becomes increasingly specialized and standardized, the precise differentiation between ≤T1 and ≥T2 gastric cancer before endoscopic intervention holds paramount clinical significance. OBJECTIVE: To evaluate the diagnostic efficacy of contrast-enhanced gastric ultrasonography in differentiating ≤T1 and ≥T2 gastric cancer. METHODS: PubMed, Web of Science, and Medline were searched to collect studies published from January 1, 2000 to March 16, 2023 on the efficacy of either double contrast-enhanced gastric ultrasonography (D-CEGUS) or oral contrast-enhanced gastric ultrasonography (O-CEGUS) in determining T-stage in gastric cancer. The articles were selected according to specified inclusion and exclusion criteria, and the quality of the included literature was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 scale. Meta-analysis was performed using Stata 12 software with data from the 2 × 2 crosslinked tables in the included literature. RESULTS: In total, 11 papers with 1124 patients were included in the O-CEGUS analysis, which revealed a combined sensitivity of 0.822 (95% confidence interval [CI] = 0.753-0.875), combined specificity of 0.964 (95% CI = 0.925-0.983), and area under the summary receiver operating characteristic (sROC) curve (AUC) of 0.92 (95% CI = 0.89-0.94). In addition, five studies involving 536 patients were included in the D-CEGUS analysis, which gave a combined sensitivity of 0.733 (95% CI = 0.550-0.860), combined specificity of 0.982 (95% CI = 0.936-0.995), and AUC of 0.93 (95% CI = 0.91-0.95). According to the I2 and P values ​​of the forest plot, there was obvious heterogeneity in the combined specificities of the included papers. Therefore, the two studies with the lowest specificities were excluded from the O-CEGUS and D-CEGUS analyses, which eliminated the heterogeneity among the remaining literature. Consequently, the combined sensitivity and specificity of the remaining studies were 0.794 (95% CI = 0.710-0.859) and 0.976 (95% CI = 0.962-0.985), respectively, for the O-CEDUS studies and 0.765 (95% CI = 0.543-0.899) and 0.986 (95% CI = 0.967-0.994), respectively, for the D-CEGUS studies. The AUCs were 0.98 and 0.99 for O-CEGUS and D-CEGUS studies, respectively. CONCLUSION: Both O-CEGUS and D-CEGUS can differentiate ≤T1 gastric cancer from ≥T2 gastric cancer, thus assisting the formulation of clinical treatment strategies for patients with very early gastric cancer. Given its simplicity and cost-effectiveness, O-CEGUS is often favored as a staging method for gastric cancer prior to endoscopic intervention.

Evaluation of AJCC staging system and proposal of a novel stage grouping system in retroperitoneal liposarcoma: the Fudan Zhongshan experience.

Background: Overall survival (OS) varies significantly among individuals with heterogeneous retroperitoneal liposarcoma (RPLS), even among those with the same clinical stage. Improved staging of RPLS is a critical unmet need, given the disappointing results of external validations of the 8th American Joint Committee on Cancer (AJCC) TNM staging system. Methods: The cohort study included 220 consecutive patients who underwent surgical resection for primary RPLS at the largest sarcoma centre of Fudan University in China from September 2009 to August 2021, combined with 277 adult patients with RPLS in the SEER database from 1975 to 2020. Data analysis was performed from December 2021 to December 2022. Patients were retrospectively restaged according to the 8th and 7th editions of the TNM staging system as well as the new TNM (nTNM) staging system. The primary endpoint was overall survival (OS). Comparative analysis of postoperative survival was performed using the Kaplan-Meier method, and differences between subgroups were tested using the log-rank test. The OS prediction nomogram was generated based on baseline variables and tumour characteristics. Harrell's consistency index (C-index), area under the curve (AUC) of receiver operating characteristic curves (ROC), and calibration curves were used to evaluate the performance of the nomogram. Results: A total of 497 patients were enrolled in the study, including 282 (56.7%) male patients. The median follow-up was 51 months (interquartile range, IQR, 23-83), and the OS rates at 1, 3, and 5 years were 87.9%, 75.3%, and 64.9%, respectively. According to the staging distribution of the AJCC 7th edition, 6 patients were stage IA (1.2%), 189 patients were stage IB (38%), 12 patients were stage IIA (2.4%), 150 patients were stage IIB (30.1%), 131 patients were stage III (26.3%), and 9 patients were stage IV (1.8%). With the 8th edition staging, this distribution changed: 6 patients (1.2%) were stage IA, 189 patients (38%) were stage IB, 12 patients (2.4%) were stage II, 24 patients (4.8%) were stage IIIA, 257 patients (51.7%) were stage IIIB, and 9 patients (1.8%) were stage IV. 182 patients (36.6%) were reclassified according to the nTNM staging system with the new T stage classification. The C-index and log-rank score improved after implementation of nTNM implementation. The nTNM system was associated with improved identification of high-risk patients compared with the AJCC 7th and 8th TNM. The FNCLCC stage proved to be highly prognostic with significant intergroup differences in OS. The calibration curve shows a high degree of agreement between the actual OS rate and the nomogram estimated OS rate. Conclusion: Compared with 8th AJCC TNM, 7th AJCC TNM staging system showed a more homogeneous staging distribution and a slight improvement in the prognostic accuracy of RPLS. The revised T-stage and nTNM systems showed better risk stratification performance. The FNCLCC stage was found to have high prognostic value, further emphasising histological grade is the least negligible prognostic factor in predicting patient survival. The constructed nomogram model enables individualized prognostic analysis and helps to develop risk-adapted therapy for RPLS patients.

Multivariate logistic regression analysis of the clinical factors influencing locally advanced prostate cancer.

Background: Locally advanced prostate cancer (PCa) carries a high risk of recurrence and metastasis after surgery, and the prognosis is poor. We explored the risk factors for locally advanced PCa among clinical factors (neutrophil: lymphocyte ratio, lymphocyte: monocyte ratio) and indicators of systemic inflammation [prostate-specific antigen (PSA) level, Gleason score, body mass index (BMI)] through retrospective evaluation of patients with PCa diagnosed at our center. The pathologic T stage was a key indicator of locally advanced PCa. Methods: Data from patients with pathologically confirmed PCa at our center from 1 January 2015 to 1 May 2020 were collected in strict accordance with inclusion and exclusion criteria. Clinical data were collected and the relationship between the indicators and the pathologic T stage was explored. First, Spearman rank correlation analysis was used to find the correlates of the pathologic T stage. Then, logistic ordered multiple regression analysis was used to identify independent risk factors. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracy for the T stage of PCa. Results: After rigorous screening, the data of 177 patients were obtained. Spearman correlation analysis showed that BMI, the PSA level, Gleason score, hypertension, N stage, and M stage were significantly correlated with the T stage (P<0.05), suggesting that these factors may be involved in locally advanced PCa. Analyses of ROC curves showed that the PSA level [area under the ROC curve (AUC) =0.802] had greater value than BMI (0.675) for the diagnosis of the pathologic T stage PCa, and that a combination of BMI and PSA (combined AUC =0.822) could improve locally advanced PCa diagnosis. Conclusions: BMI and PSA are independent risk factors for locally advanced PCa. They may play a key part in locally advanced PCa.

Effect of T Stages on the Choice of Axillary Evaluation Modality in Breast Cancer Patients With 1-2 Sentinel Lymph Node Metastases.

INTRODUCTION: The survival benefit of axillary lymph node dissection (ALND), sentinel lymph node biopsy (SLNB) combined with radiation, and ALND combined with radiation remains unclear in breast cancer (BC) patients with 1-2 metastatic sentinel lymph nodes (SLNs). This study aims to rigorously evaluate the prognostic impact of these axillary evaluation modalities on BC patients with varying T-stages and to construct a survival prediction nomogram. METHODS: Following screening for inclusion and exclusion criteria, data pertaining to BC patients were extracted from the SEER database. Overall survival (OS) and breast cancer-specific survival (BCSS) were assessed using Kaplan-Meier curves and Cox proportional hazards model among patients with different stages who underwent various axillary evaluation modalities. A nomogram was constructed to predict the probability of OS and BCSS. RESULTS: A total of 20,283 patients were included, comprising 9626 who underwent breast-conserving surgery (BCS) and 10,657 who underwent mastectomy. In the T4 stage stratified analysis, both BCS and mastectomy groups exhibited superior OS and BCSS with ALND compared to SLNB combined with radiation. Further, ALND combined with radiation improved OS. However, for T1-3 stages, patients treated with ALND experienced similar or worse survival compared to those treated with SLNB combined with radiation. The calibration curve and C-index (0.746-0.794) of the nomogram demonstrated the efficacy of the survival prediction model. CONCLUSION: In T1-3 BC patients with 1-2 metastatic SLNs, SLNB combined with radiation is a safe alternative to ALND. Conversely, for T4 patients, ALND combined with radiation may offer a preferable choice.

Association between CT-based adipose variables, preoperative blood biochemical indicators and pathological T stage of clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification. Methods: This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables. Results: In specific intervals, fibrinogen levels (FIB) (2.63-4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (<200.34 × 109/L) were significantly negatively correlated with T stage. Additionally, tumor-associated material exhibited varying degrees of positive correlation with T stage at different cut-off intervals (cut-off value: 90.556 U/mL). Conclusion: Preoperative PLT, FIB and TAM are nonlinearly related to pathological T stage. This study is the first to provide specific cut-off intervals for preoperative variables that are nonlinearly related to ccRCC T stage. These intervals can aid in the risk stratification of ccRCC patients before surgery, allowing for developing a more personalized treatment planning.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Updates on lung adenocarcinoma: invasive size, grading and STAS.

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Conjoint analysis of clinical, imaging, and pathological features of schistosomiasis and colorectal cancer.

This study aims to examine and compare clinical, radiological, and pathological data between colorectal cancer (CRC) patients with and without schistosomiasis and uncover distinctive CRC characteristics when accompanied by schistosomiasis. This retrospective study is based on data collected from 341 patients diagnosed with CRC post-surgery and pathology. Of these patients, 101 (Group A) were diagnosed with colorectal cancer co-occurring with schistosomiasis (CRC-S), while 240 patients (Group B) were diagnosed with colorectal cancer without concurrent schistosomiasis (CRC-NS). Both groups were compared and analyzed based on their clinical data, imaging-based TNM staging, lymph node metastasis, nerve invasion, vascular cancer thrombus, and histopathological differentiation. A Chi-squared test revealed a significant difference in gender distribution between the patients with CRC-S (Group A) and CRC-NS (Group B), with a p -value of 0.043 and χ2 = 4.115. Specifically, a higher incidence rate was observed among males in Group A. There was a difference in the overall distribution of TNM staging between the two groups (p = 0.034, χ2 = 6.764). After pairwise comparison, a statistically significant difference was observed in the T3 stage (p <0.05). The proportion of the T3 stage in Group A was significantly higher than that in Group B, indicating certain advantages. There was a difference in postoperative histopathological grading between the two groups (p = 0.005, χ2 = 10.626). After pairwise comparison, a statistically significant difference was observed between the well-differentiated adenocarcinoma and the moderately and poorly differentiated adenocarcinoma (p <0.05), with a higher proportion of welldifferentiated patients in Group A compared to Group B. There was no significant difference in age, lymph node metastasis, nerve invasion, and vascular invasion between the two groups of patients (p > 0.05). Among the 101 patients with CRC-S, 87 (86%) showed linear calcification on CT imaging. Patients with CRC-S are mainly male, with tumor staging mostly in the middle stage, high tumor differentiation, and low malignancy. CT imaging can help identify the presence of lumps and linear calcification indicative of schistosome deposits. MRI can early clarify TNM staging and determine the presence of lymph node metastasis and nerve and vascular invasion.

Differentiation of bladder cancer stages using the vesical imaging -reporting and data system and apparent diffusion coefficient.

Background: T stage is closely related to the treatment and prognosis of patients with bladder cancer (BC). However, preoperative T staging is still challenging. Multiparametric magnetic resonance imaging (mpMRI) may be valuable. This study was performed to explore the value of the Vesical Imaging-Reporting and Data System (VI-RADS) and the volumetric apparent diffusion coefficient (ADC) histogram parameters in detecting T2 stage and below stage (≤T2 stage) from T3 stage and above stage (≥T3 stage) BCs. Methods: The study included 62 patients (mean age, males vs. females: 62.1±10.9 vs. 61.8±11.7 years) with BC pathologically confirmed by partial or radical cystectomy. All of the tumors were scored normatively by two radiologists using the VI-RADS scoring system by two radiologists. The volumetric ADC histogram of each lesion was obtained from the ADC maps. The Cochran-Armitage test was used to examine the relevance between VI-RADS scores and T stages. The Mann-Whitney U test was used to compare the histogram parameters between ≤T2 stage and ≥T3 stage BCs. A receiver operating characteristic (ROC) curve was used to assess the predictive power of each model. Results: The minimum ADC; mean ADC; median ADC; maximum ADC; and 10th, 25th, 75th, and 90th percentile ADC of ≤T2 stage BCs were significantly higher than those of ≥T3 stage BCs, while skewness and kurtosis had opposite results. VI-RADS achieved the highest area under the curve (AUC) of 0.834 among all parameters. The combination of VI-RADS, skewness and kurtosis yield a significantly higher AUC than VI-RADS alone (0.915 vs. 0.834, P=0.0478). Conclusions: VI-RADS and volume ADC histogram analysis can effectively discriminate between ≤T2 stage and ≥T3 stage BCs, and the volumetric ADC histogram can provide further information to supplement VI-RADS.

Prognostic and Clinical Implications of UNC13C expression in Hepatocellular Carcinoma Patients.

Aberrant expression of UNC13C (Unc-13 Homolog C) has been observed during the progression of oral squamous cell carcinoma. However, the expression pattern and clinical relevance of UNC13C in Hepatocellular carcinoma (HCC) remain to be elucidated. The purpose of this study is to examine UNC13C expression in HCC and explore its role in clinicopathological factor or prognosis in HCC. Two hundred and sixty-five patients diagnosed with HCC were included in the present study. The expression of UNC13C in HCC tissues was analyzed by immunohistochemistry analysis. The relationship between UNC13C protein and clinicopathological characteristics in HCC was investigated. Moreover, the high expression of UNC13C was significantly correlated with T stage, AJCC stage and overall survival rates. Cox regression analysis identified UNC13C as an independent prognostic indicator for HCC patients. UNC13C might be a prognostic biomarker and therapeutic target in HCC. Further studies with larger sample sets are needed to understand the clinical implications of UNC13C in hepatocellular carcinoma.

Development and validation of a clinical cure marker based on negative lymph nodes for gastric cancer after gastrectomy.

Objective: To explore lymph node (LN)-related derived indicators as clinical cure markers for gastric cancer (GC) after gastrectomy. Methods: Data of resected GC patients were extracted from the SEER database and our own department. Propensity score matching (PSM) was used to balance the baseline differences between the clinical cure and the nonclinical cure groups. The area under the curve (AUC) and decision curve analysis (DCA) were used to choose the optimal marker, and survival analysis was used to validate the clinical value of the most effective marker. Results: After PSM, the differences in age, sex, race, location, surgical type, and histologic type between the two groups were significantly reduced (all P > 0.05), and the AUCs of examined LNs (ELNs), negative LNs (NLNs), ESR (ELNs/tumor size), ETR (ELNs/T-stage), NSR (NLNs/tumor size), NTR (NLNs/T-stage), EPR (ELNs/PLNs) and NPR (NLNs/PLNs) were 0.522, 0.625, 0.622, 0.692, 0.706, 0.751, 7.43, and 7.50, respectively. When NTR was 5.9, the Youden index of 0.378 was the highest. The sensitivity and specificity were 67.5% and 70.3% in the training group and 66.79% and 67.8% in the validation group, respectively. DCA showed that NTR had the largest net clinical benefit, and patients with NTR greater than 5.9 had significantly prolonged overall survival in our own cohort. Conclusion: NLNs, NTR, NSR, ESR, ETR, NPR and EPR can be used as clinical cure markers. However, NTR was the most effective, and the best cutoff value was 5.9.