Lymphovascular Invasion Status Research Articles

Reassessment reveals underestimation of infiltration depth in surgical resection specimens with lymph-node positive T1b esophageal adenocarcinoma

AbstractEndoscopic resection (ER) has proven effective and safe for T1 esophageal adenocarcinoma (EAC). However, uncertainty remains concerning risk-benefit return of esophagectomy for submucosal lesions (T1b). Surgical series in past decades have reported significant risk of lymph node metastasis (LNM) in T1b EAC, but these rates may be overestimated due to limitations in histological assessment of surgical specimens. We aimed to test this hypothesis by reassessing histological risk features in surgical specimens from T1b EAC cases with documented LNM.A retrospective cross-sectional study (1994–2005) was conducted. Patients who underwent direct esophagectomy without prior neoadjuvant therapy for suspected T1b EAC with LNM were included. Additional tissue sections were prepared from archival tumor blocks. A consensus diagnosis on tumor depth, differentiation grade, and lymphovascular invasion (LVI) was established by a panel of experienced pathologists.Specific depth of submucosal invasion (sm1 to sm3) was not specified in 10 of 11 archival case sign-out reports. LVI status was not reported in seven of 11 cases. Following reassessment, one patient was found to have deep tumor invasion into the muscularis propria (T2). The remaining 10 of 11 patients exhibited deep submucosal invasion (sm2–3), with five showing one or more additional risk features (poor differentiation and/or LVI).Our findings highlight the potential for underestimating tumor depth of invasion and other high-risk features in surgical specimens. Despite the limited cohort size, our study confirmed a consistent high-risk histological profile across all cases. Caution is warranted when extrapolating LNM risk data from historic heterogeneous cross-sectional surgical cohorts to the modern ER era.

Exploring the Prognostic Role of Trop-2, CD47, and CD163 Expression Levels on Survival Outcomes in Patients with Triple-Negative Breast Cancer.

Background: This study evaluated the prognostic impact of Trop-2, CD47, and CD163 expression on clinical outcomes in triple-negative breast cancer (TNBC) and investigated their interactions with tumor progression. Methods: A retrospective cohort of 92 patients with TNBC was analyzed. The expression scores for Trop-2, CD47, and CD163 were categorized as negative/low (0-3 points) or high (4-6 points). The primary endpoint was overall survival (OS). Results: The median age of the cohort was 50 years old. High Trop-2 expression was observed in 55.4% of the patients and was significantly associated with advanced disease stage (p < 0.001). High CD47 expression (44.6%) was correlated with advanced stage (p = 0.044), whereas high CD163 expression (45.7%) was associated with advanced stage (p = 0.021), absence of comorbidities (p = 0.022), and lower pT stage (p = 0.023). Moderate positive correlations were found between Trop-2 and CD47 (p = 0.022), Trop-2 and CD163 (p = 0.037), and CD47 and CD163 (p < 0.001), respectively. Kaplan-Meier survival analysis revealed that patients with low Trop-2 expression exhibited significantly prolonged OS (p = 0.021) and progression-free survival (PFS) (p = 0.026) compared to those with high Trop-2 expression. Univariate and multivariate analyses revealed significant associations between OS and PFS for Trop-2, lymphovascular invasion, and BRCA status. Conclusions: Trop-2 expression is a significant prognostic factor for TNBC and is correlated with worse outcomes. Although CD47 and CD163 showed trends for poorer prognosis, their significance was not confirmed. These findings offer promising prospects for future studies on combined antibody-drug conjugates (ADCs), as they may present opportunities to address multiple resistance mechanisms in the management of TNBC and enhance clinical outcomes.

A novel MRI-based radiomics for preoperative prediction of lymphovascular invasion in rectal cancer.

To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer. This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set. The clinical features and MRI imaging characteristics of the patients in the training set were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for LVI in rectal cancer, and these risk factors were then used to construct a clinical model. Regions of interest (ROIs) were delineated on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences for feature extraction. After feature reduction and selection, the most strongly correlated features were identified, and their respective regression coefficients were calculated to construct the radiomics model. Finally, a combined clinical-radiomics model was built using a weighted linear combination of features and was visualized as a nomogram. The predictive performance of each model was quantified using receiver operating characteristics (ROC) curves and the area under the curve (AUC) in both training and validation sets, with DeLong analysis being used to compare model performance. Decision curve analysis (DCA) was used to evaluate the clinical utility of each model in the validation sets. In the 239 patients, the combined model outperformed the clinical and radiomics models in predicting LVI in rectal cancer. The combined model showed excellent predictive performance in the training, internal validation, and external validation sets, with AUCs of 0.90 (0.88-0.97), 0.88 (0.78-0.99), and 0.88 (0.78-0.95), respectively. The sensitivity values were 75.9%, 68.8%, and 80.0%, respectively, and the specificity values were 90.3%, 92.7%, and 88.6%. DCA results indicated that the nomogram of the combined model had superior clinical utility compared with the clinical and radiomics models. The clinical-radiomics nomogram serves as a valuable tool for non-invasive preoperative prediction of LVI status in patients with rectal cancer.

Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model

To prevent the overaggressive treatment of axillary lymph nodes (ALNs) in breast cancer, it is necessary to develop a convenient analysis method that accurately and comprehensively reflects whether ALNs are metastatic or nonmetastatic. We retrospectively analyzed data from patients who underwent surgery for breast cancer at the Weifang Hospital of Traditional Chinese Medicine between January 2019 and June 2023. Binary logistic regression analysis was used to predict the metastasis status of ALNs. The developmental data set included 531 patients (January 2019–June 2023). The validation set included separate data points (n = 178, January 2019–June 2023). Multivariate analysis revealed that positive findings on breast physical examination, ultrasound grades of ALNs, lymphovascular invasion, and Her-2 status had significant predictive value for metastatic ALNs. Based on these findings, a 5-grade risk scoring system and 3-level management recommendations were developed. The risk of metastasis ranged from 11.25 to 93.46%, which was positively correlated with an increase in risk grade. The areas under the curve of the development and validation sets were 0.895 and 0.865, respectively. Ultimately, a convenient, accurate and comprehensive web-based predictive model was constructed using various breast cancer clinical, imaging and pathological criteria to stratify ALNs according to the metastasis probability.

Development and validation of a multi-parametric MRI deep-learning model for preoperative lymphovascular invasion evaluation in rectal cancer.

Lymphovascular invasion (LVI) is an independent prognostic factor for patients with rectal cancer (RC). Recent studies have shown that deep learning (DL)-based magnetic resonance imaging (MRI) has potential in evaluating the treatment response of RC patients, but the role of MRI-based DL in assessing RC LVI remains unclear. This study sought to develop and validate a DL model to evaluate the LVI status of RC patients preoperatively based on MRI, and to test its performance at an external center. The data of 489 patients with surgically confirmed RC were retrospectively collected from two centers. The training set and the internal validation set comprised 320 patients and 80 patients, respectively, from The Second Affiliated Hospital of Harbin Medical University; while the external testing set comprised 89 patients from Xinjiang Production and Construction Corps Tenth Division Beitun Hospital. All the patients underwent MRI examinations before surgery. Two separate image models were constructed based on the three-dimensional (3D) residual network (ResNet)-18 architecture, using only T2-weighted image (T2WI) data and diffusion-weighted image (DWI) data, respectively, to assess LVI, and a combined model was developed that integrated T2WI, DWI, and clinical factors to assess LVI. The performance of the T2WI- and DWI-based models, and the combination model was evaluated using the area under the curve (AUC) and the DeLong test. The clinical utility of these models was assessed by calibration curve analysis and decision curve analysis (DCA). The T2WI- and DWI-based DL models demonstrated robust capabilities in evaluating LVI in RC in both the internal validation set and the external test set. For the T2WI-based model, the AUC values reached 0.795 and 0.764 in the internal validation set and the external test set, respectively. For the DWI-based model, the AUC values reached 0.822 and 0.825 in the internal validation set and the external test set, respectively. The combined model exhibited superior performance, achieving AUC values of 0.899 and 0.848 in the internal validation set and the external test set, respectively. In the external test set, all three DL models exhibited robust calibration. The DCA also showed that the DWI-based model and the combined model offered a significantly greater overall net benefit in evaluating LVI than the T2WI-based model. The multi-parametric MRI DL model demonstrated excellent performance in evaluating the LVI status of patients with RC. This model could serve as a complementary method for the non-invasive assessment of LVI in RC.

Prognostic significance of lymphovascular invasion in pN0 stage gastric cancer: a propensity score matching analysis

BackgroundTo explore the potential impact of lymphovascular invasion (LVI) on overall survival (OS) of pN0 stage gastric cancer (GC) after curative resection.MethodsA total of 497 GC patients who underwent curative gastrectomy and postoperative pathology proved negative lymph node metastasis between January 2015 and December 2018 in our center were enrolled in this study. All patients were divided into two groups according to the status of LVI. Their clinical and pathological features were compared and potential prognostic factors were analyzed using the propensity score matching analysis (PSM).ResultsNinety-nine (19.9%) patients had LVI. The presence of LVI was associated with significantly worse survival outcomes in both the overall and PSM cohorts (χ2 = 19.635, p < 0.001; χ2 = 9.367, p = 0.002). After PSM, data of 99 pairs of patients were extracted. Multivariate analysis revealed that number of examined lymph nodes (LNs), and LVI were independent predictors of OS (all p < 0.05). Following stratified analysis, patients with LNs 11–25 and those without LVI tended to have better OS than those with LVI (LNs 11–15: χ2 = 5.019, p = 0.025; LNs 16–25: χ2 = 11.876, p = 0.001).ConclusionspN0 stage GC patients with LVI have poor prognosis. More than 15 lymph nodes need to be dissected to reduce the influence of LVI on the prognosis of pN0 stage GC patients.

MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer

ObjectiveLymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC.MethodsA total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models.ResultsEighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively.ConclusionThe eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates.

MRI Radiomics-Based Machine Learning to Predict Lymphovascular Invasion of HER2-Positive Breast Cancer.

This study aims to develop and prospectively validate radiomic models based on MRI to predict lymphovascular invasion (LVI) status in patients with HER2-positive breast cancer. A total of 225 patients with HER2-positive breast cancer who preoperatively underwent breast MRI were selected, forming the training set (n = 99 LVI-positive, n = 126 LVI-negative). A prospective validation cohort included 130 patients with breast cancer from the Affiliated Zhongshan Hospital of Dalian University (n = 57 LVI-positive, n = 73 LVI-negative). A total of 390 radiomic features and eight conventional radiological characteristics were extracted. For the optimum feature selection phase, the LASSO regression model with tenfold cross-validation (CV) was employed to identify features with non-zero coefficients. The conventional radiological (CR) model was determined based on visual morphological (VM) features and the optimal radiomic features correlated with LVI, identified through multivariate logistic analyses. Subsequently, various machine learning (ML) models were developed using algorithms such as support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting machine (GBM), and random forest (RF). The performance of ML and CR models. The results show that the AUC of the CR model in the training and validation sets were 0.81 (95% confidence interval [CI], 0.74-0.86) and 0.82 (95% CI, 0.69-0.89), respectively. The ML model achieved the best performance, with AUCs of 0.96 (95% CI, 0.99-1.00) in the training set and 0.95 (95% CI, 0.89-0.96) in the validation set. There were significant differences between the CR and ML models in predicting LVI status. Our study demonstrated that the machine learning models exhibited superior performance in predicting LVI status based on pretreatment MRI compared to the CR model, which does not necessarily rely on a priori knowledge of visual morphology.

ASO Author Reflections: How to Efficiently and Accurately Predict the Lymphovascular Invasion Status of Muscle-Invasive Bladder Cancer?

ASO Author Reflections: How to Efficiently and Accurately Predict the Lymphovascular Invasion Status of Muscle-Invasive Bladder Cancer?

A nomogram model to predict recurrence of early-onset endometrial cancer after resection based on clinical parameters and immunohistochemical markers: a multi-institutional study.

This study aimed to investigate the prognosis value of the clinical parameters and immunohistochemical markers of patients with early-onset endometrial cancer (EC) and establish a nomogram to accurately predict recurrence-free survival (RFS) of early-onset EC after resection. A training dataset containing 458 patients and an independent testing dataset consisting of 170 patients were employed in this retrospective study. The independent risk factors related to RFS were confirmed using Cox regression models. A nomogram model was established to predict RFS at 3 and 5 years post-hysterectomy. The C-index, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and calibration curve were calculated to assess the predictive accuracy of the nomogram. In all early-onset EC patients, more than half (368/628, 58.6%) were diagnosed in the age range of 45-49 years. Meanwhile, the recurrence rate of early-onset EC is approximately 10.8%. Multivariate Cox regression analyses showed that histological subtype, FIGO stage, myometrial invasion, lymphovascular space invasion (LVSI), P53 expression, and MMR status were independent prognostic factors related to RFS (all P < 0.05) and established the nomogram predicting 3- and 5-year RFS. The C-index and calibration curves of the nomogram demonstrated a close correlation between predicted and actual RFS. Patients were divided into high- and low-risk groups according to the model of RFS. Combining clinical parameters and immunohistochemical markers, we developed a robust nomogram to predict RFS after surgery for early-onset EC patients. This nomogram can predict prognosis well and guide treatment decisions.

Validation of lymphovascular invasion as a predictor of lymph-node invasion in squamous cell carcinoma of the penis.

The aim of this study was to validate lymphovascular invasion (LVI) as a predictor of lymph-node invasion (LNI) in squamous cell carcinoma of the penis (SCCP). Within the Surveillance, Epidemiology, and End Results database (2010-2020), we identified SCCP patients who underwent lymphadenectomy with known LVI status. Univariable logistic regression models (LRMs) addressed LNI. Harrell's concordance index (c-index) quantified accuracy after 2000 bootstrap resamples for internal validation. Multivariable LRMs included the most informative, statistically significant predictors. Subgroup analyses were repeated in organ-confined (T1b-T2) and non-organ confined (T3-T4) stages. Of 586 SCCP patients, 219 (37%) had LVI. LVI was associated with higher rate of LNI (66 vs. 43%; P<0.001). Positive predictive value of LVI was 66 vs. 57% for negative predictive value. In multivariable LRMs, LVI independently predicted LNI (Odds ratio [OR]: 2.41; P<0.001). Bootstrap-adjusted c-index of multivariable model was 0.570 without LVI vs. 0.639 with LVI. In subgroup analyses, LVI independently predicted LNI in organ-confined (OR: 2.23; P<0.001) and in non-organ confined stages (OR: 3.10; P<0.001). In subgroup analyses, addition of LVI increased c-index from 0.530 to 0.595 in organ-confined and from 0.599 to 0.682 in non-organ confined. The current study validates LVI as an independent predictor of LNI in SCCP. LVI increases the accuracy of LNI predictions in the overall cohort as well as in organ-confined and non-organ confined stages. However, stage and grade even with the added consideration of LVI are not accurate enough to provide LNI prediction in individual patients.

Nevus-associated acral melanoma has lower risk of recurrence and mortality than de novo acral melanoma: A multicenter, retrospective analysis of 482 patients

BackgroundIt is unknown whether acral melanomas (AM) associated with pre-existing nevi have similar risk to other acral melanomas. ObjectiveTo compare risk of recurrence and death between AM associated with pre-existing nevi and de novo AM. MethodsWe conducted a multicenter retrospective cohort study involving patients diagnosed with AM between February 2011 and November 2022. Results164 patients (34.0%) had nevus-associated acral melanoma (NAAM). NAAM has lower risk of recurrence (HR 0.69, 95% CI 0.52–0.91, P = 0.008) and mortality (HR 0.53, 95% CI 0.37–0.76, P < 0.001) than de novo acral melanoma (DNAM). The five-year recurrence-free survival (RFS) was 55.1% for NAAM compared to 42.3% for DNAM. The five-year overall survival (OS) was 75.1% for NAAM and 63.2% for DNAM. Multivariate analyses identified Breslow thickness, nevus association, and sentinel node status as independent predictors of RFS. Age, Breslow thickness, nevus association, lymphovascular invasion, ulceration, and sentinel node status were independent predictors of OS. LimitationsThe study's limitations included its retrospective design and missing data. ConclusionPatients with nevus-associated AM had significantly better RFS and OS compared to those with de novo AM. Nevus association was an independent prognostic factor for both RFS and OS.

Improving Prognostic Value in Invasive Triple Negative Breast Cancer Through a Combined Nomogram Approach

ObjectivesTo investigate the potential prognostic value of ultrasound (US) features in conjunction with pathological markers and to develop a preliminary working model for predicting poor outcomes in patients with invasive triple-negative breast cancer (TNBC). MethodsFrom January 2012 to December 2018, we enrolled 209 TNBC patients treated with standard therapy, systematically gathered data on US parameters, stromal tumor-infiltrating lymphocytes (TILs), lymphovascular invasion (LVI) status, and other relevant information, and recorded follow-up data. A nomogram combining AJCC staging with US score, stromal TILs, and LVI was constructed and validated to predict poor outcomes, defined as recurrence or death, in patients with invasive TNBC. ResultsThe US score of 4 was best related to poor outcomes in patients with TNBC (HR 3.87, p = 0.015). In the training set, the nomogram had a considerably greater prognostic value [area under the curve (AUC), 0.74 vs. 0.64, p=0.045] than AJCC staging alone, and it was comparable to that of the validation set (AUC, 0.71 vs. 0.63, p=0.804). An acceptable consistency between the nomogram-predicted and actual survival probabilities was found both in the training and validation sets, with Brier scores of 0.15 and 0.13, respectively. ConclusionsThe incorporation of AJCC stage with US score, stromal TILs, and LVI improved the model performance for predicting poor outcomes in patients with invasive TNBC compared to routine AJCC staging alone. MicroAbstractA nomogram was developed to predict the disease-free survival of invasive triple-negative breast cancer (TNBC) patients who had received standard therapy. The nomogram was constructed using ultrasound features and pathological variables, including stromal tumour-infiltrating lymphocytes as well as lymphovascular invasion, in conjunction with the AJCC stage. The nomogram was developed using data from 209 TNBC patients. The combined model improved the predictive performance compared to routine AJCC staging, and might help to identify patients at high risk of poor outcomes.

Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.

Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment. We aim to develop a deep learning model to identify the LVI status in whole-slide images (WSIs) of MIBC patients. A cohort from The Cancer Genome Atlas (TCGA) database was used to train a deep learning model, slide-based lymphovascular invasion predictor (SBLVIP), based on multiple-instance learning. This model was externally validated using the Renmin Hospital of Wuhan University (RHWU) and People's Hospital of Hanchuan City (PHHC) cohorts. Kaplan-Meier curves, along with univariate and multivariate Cox models, were employed to evaluate the association between the LVI status predicted by SBLVIP and the survival outcomes of MIBC patients. In the TCGA cohort, the SBLVIP model achieved an average accuracy of 0.804 [95% confidence interval (CI) 0.712-0.895] and an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.63-0.84) in the training set. In the internal validation set, the model's average accuracy and AUC were 0.774 (95% CI, 0.701-0.846) and 0.76 (95% CI, 0.60-0.83), respectively. In the RHWU cohort, the SBLVIP model achieved an average accuracy of 0.807 (95% CI 0.734-0.880) and an AUC of 0.74 (95% CI 0.55-0.83). In the PHHC cohort, SBLVIP demonstrated an average accuracy of 0.821 (95% CI 0.737-0.909) and an AUC of 0.74 (95% CI 0.58-0.89). Moreover, the LVI status predicted by SBLVIP showed significant independent prognostic value (P = 1 × 10-6). We developed a deep learning model named SBLVIP to predict the LVI status in routine WSIs of MIBC patients.

Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?

BackgroundTo compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).MethodsTwo hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors’ 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).ResultsThere were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.ConclusionsBoth 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.

Combination of single-source dual-energy computed tomography (CT) parameters and extracellular volume fraction for predicting lymphovascular and perineural invasion in colorectal cancer.

Lymphovascular invasion (LVI) and perineural invasion (PNI) are important histopathological variables that are directly related to the survival and recurrence of patients with colorectal cancer (CRC). Preoperative prediction of LVI and PNI status in CRC is helpful in selecting patients requiring appropriate adjuvant therapy and evaluating prognosis. This study aimed to investigate the value of combining single-source dual-energy computed tomography (ssDECT)-derived parameters with extracellular volume (ECV) fraction for preoperative evaluation of LVI and PNI in CRC. This retrospective study included patients with CRC who underwent contrast-enhanced ssDECT. All diagnoses were confirmed through histopathology, and the patients were classified into positive and negative groups based on the presence of LVI/PNI. Clinical data were collected. In the arterial (AP), venous (VP) and delayed phases (DP), the ssDECT-derived parameters were measured by two radiologists. The measurement consistency was evaluated using intraclass correlation coefficients. Differences between the two groups were analyzed using the t-test, Mann-Whitney U test, or Chi-square test. Binary logistic regression was employed to construct models incorporating multiple parameters. The diagnostic performance of various parameters or models was assessed by analyzing receiver operating characteristic curves. In total, 118 patients with CRC were included in the study. Serum carcinoembryonic antigen levels, T and N stages, and histological grades differed between the two groups (all P<0.05). The ssDECT-derived parameters in the VP and DP of LVI/PNI-positive group were higher than those of -negative group (all P<0.05). The ECV fraction in the DP of LVI/PNI-positive group was higher than that of -negative group (P=0.001). Discriminating capability analysis demonstrated that the diagnostic efficacies of the DP parameters were superior to those of the VP parameters, and the normalized iodine concentration in the DP exhibited the best performance [area under the curve (AUC): 0.750; 95% confidence interval (CI): 0.648-0.852]. The combination of ECV DP with clinical and ssDECT-derived parameters demonstrated the highest discriminative capability (AUC: 0.857; 95% CI: 0.786-0.928). ssDECT-derived parameters and ECV fraction may serve as non-invasive tools for predicting the LVI/PNI status in CRC.

Excluding Upper Axillary Level 1 in Regional Nodal Irradiation Does Not Increase Axillary Recurrence Risk in Patients With Breast Cancer

PurposeThe optimal extent of regional nodal irradiation (RNI) in postoperative radiotherapy for breast cancer, particularly regarding axillary level 1 (AXL1), remains uncertain. This study aims to compare clinical outcomes between extensive RNI including the entire axilla and limited RNI excluding the upper AXL1 in patients with breast cancer. MethodsA retrospective analysis included 1,780 women with non-metastatic unilateral breast cancer who underwent RNI during postoperative radiotherapy between 2007 and 2018. Patients were classified into extensive and limited RNI groups based on the upper AXL1 inclusion in the radiation field. Propensity score matching yielded a cohort of 1,020 patients. Non-inferiority of limited RNI compared to extensive RNI was assessed with a defined margin of ≤2% in the 5-year axillary recurrence rate. ResultsAfter a median follow-up of 67.9 months, the 5-year axillary recurrence rates were similar between extensive and limited RNI groups (1.2% vs. 1.6%; Plog-rank=0.790). Limited RNI demonstrated non-inferiority with a 0.4% difference (95% confidence interval, -1.1%–1.9%; Pnon-inferiority=0.019). Disease-free survival (87.9% vs. 91.5%; Plog-rank=0.122) and overall survival (94.1% vs. 96.9%; Plog-rank=0.260) at 5 years were not significantly different between extensive and limited RNI groups. Multivariable analysis revealed that lymphovascular invasion (hazard ratio [HR], 5.17; P=0.02) and negative hormone receptor status (HR, 11.73; P=0.002) were associated with a higher risk of axillary recurrence, while limited RNI showed no significant association (HR, 1.35; P=0.652). Subgroup analysis demonstrated that extensive RNI did not improve axillary control in patients with lymphovascular invasion, hormone receptor negativity, positive lymph node metastasis, or a small number of nodes removed. ConclusionLimited RNI, excluding the upper AXL1 from the radiation field, demonstrated axillary recurrence rates comparable to those of extensive RNI in patients with breast cancer. The study suggests that extensive RNI may not provide additional therapeutic benefits, while limited RNI appears to be a valid option for regional control.

The effect of total size, area, and volume of lesions in multifocal/multicentric breast cancers and unifocal breast cancers on survival: An observational study.

In this study, we aimed to investigate the prognostic effect of the classifications made according to the stage of the largest lesion diameter (T-max stage) and of the sum of the longest diameters of the lesions (T-sum stage), the largest area stage (A-max stage), the sum of the largest areas (A-sum stage), the highest volume stage (V-max stage), the sum of the highest volume (V-sum stage) on disease-free survival, and overall survival (OS) in multifocal/multicentric breast cancers (MMBCs) and unifocal breast cancers (UBCs). The study included a total of 769 patients either with MMBC (n = 128) or UBC (n = 641) who underwent surgery between 2006 and 2015. In the analysis, the median age of 769 patients was 53.0 (20.0-94.0) years, and 16.6% of these 769 patients were MMBC and 83.4% were UBC. In multivariate analysis, lymphovascular invasion (LVİ), estrogen receptor, and nodal status were common independent prognostic factors, whereas T-max stage [(HR: 1.17) (CI 95%: 1.03-1.33) (P = .018)] was a prognostic factor for OS. In multivariate analysis, the T-max stage is an independent risk factor for OS. Therefore, T-max should be continued to be used for measurement and T-stage should be used for classification in MMBCs/UBCs.

Role of Ultrasonographic and Clinicopathological Characteristics in Assessing Stromal Tumor-Infiltrating Lymphocyte Density in Triple-Negative Breast Cancer

A high density of stromal tumor-infiltrating lymphocytes (sTILs) is positively correlated with the pathological complete response rate and favorable survival in patients with triple-negative breast cancer (TNBC). Heterogeneity in stromal lymphocyte distribution and limited tumor sampling may affect the accuracy of sTIL quantification in biopsy samples from patients receiving neoadjuvant therapy. Thus, identifying additional biomarkers to complement sTIL evaluation is essential. To identify biomarkers that could be used to complement sTILs evaluation. Retrospective cohort study. A total of 162 patients with invasive TNBC were enrolled in the study. The following data were gathered: sTIL density, Ki67, nuclear grades of cancer cells, lymphovascular invasion status, the American Joint Committee on Cancer stage, axillary lymph node metastasis status, and ultrasonographic parameters of the tumor (size, shape, orientation, margin, internal echo pattern, posterior feature, and vascularity). The relationship between sTIL density and ultrasonographic or clinicopathological characteristics was investigated using both continuous and categorical analyses. Posterior features of the primary tumors was associated with sTIL density (p = 0.038). Additionally, the Ki67 levels and nuclear grades were also associated with sTIL density (p < 0.001 and p = 0.024, respectively). When stratified according to a 20% cut-off of sTIL density, the posterior features of primary tumors, Ki67 levels, and nuclear grades significantly differed between the high and low sTIL density tumors. Tumors with high Ki67 levels were more likely to exhibit high sTIL density than low sTIL density [odds ratio (OR): 2.75, p = 0.021]. Furthermore, nuclear grade III tumors demonstrated significantly higher sTIL density than nuclear grade I-II tumors (OR: 2.49, p = 0.014). Additionally, tumors with posterior enhancement or no posterior features were more likely to exhibit high sTIL density than tumors with acoustic shadows (OR: 2.91, p = 0.028; OR: 2.74, p = 0.022, respectively). Low sTIL density is frequently observed in tumors exhibiting acoustic shadows on ultrasound. However, high sTIL density is more common in tumors with posterior enhancement or no posterior features. Furthermore, high Ki67 levels (> 40%) and high nuclear grades are positively correlated with high sTIL density. This study findings highlight the need for closer surveillance of these biomarkers to complement sTIL evaluation in TNBC.

Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.

This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve. Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591-0.878) for PNI in the training set and 0.731 (95% CI: 0.509-0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561-0.841), and the validation set AUC is 0.685 (95% CI: 0.439-0.932). External validation showed an AUC of 0.722 (95% CI: 0.565-0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481-0.931) for LVI. This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.