Lymphovascular Invasion Status Research Articles

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.

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.

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.

Improved Risk Prediction in Human Papillomavirus-Associated Endocervical Adenocarcinoma Through Assessment of Binary Silva Pattern-based Classification: An International Multicenter Retrospective Observational Study Led by the International Society of Gynecological Pathologists (ISGyP).

Endocervical adenocarcinomas (EACs) are a group of malignant neoplasms associated with diverse pathogenesis, morphology, and clinical behavior. As a component of the International Society of Gynecological Pathologists International Endocervical Adenocarcinoma Project, a large international retrospective cohort of EACs was generated in an effort to study potential clinicopathological features with prognostic significance that may guide treatment in these patients. In this study, we endeavored to develop a robust human papillomavirus (HPV)-associated EAC prognostic model for surgically treated International Federation of Gynecology and Obstetrics (FIGO) stage IA2 to IB3 adenocarcinomas incorporating patient age, lymphovascular space invasion (LVSI) status, FIGO stage, and pattern of invasion according to the Silva system (traditionally a 3-tier system). Recently, a 2-tier/binary Silva pattern of invasion system has been proposed whereby adenocarcinomas are classified into low-risk (pattern A/pattern B without LVSI) and high-risk (pattern B with LVSI/pattern C) categories. Our cohort comprised 792 patients with HPV-associated EAC. Multivariate analysis showed that a binary Silva pattern of invasion classification was associated with recurrence-free and disease-specific survival (P < 0.05) whereas FIGO 2018 stage I substages were not. Evaluation of the current 3-tiered system showed that disease-specific survival for those patients with pattern B tumors did not significantly differ from that for those patients with pattern C tumors, in contrast to that for those patients with pattern A tumors. These findings underscore the need for prospective studies to further investigate the prognostic significance of stage I HPV-associated EAC substaging and the inclusion of the binary Silva pattern of invasion classification (which includes LVSI status) as a component of treatment recommendations.

Comparing CD10 Expression With the Clinicopathological Features and Hormone Status of Invasive Breast Cancer.

Background Worldwide, female breast cancer is the most common cancer (11.7%), followed by lung (11.4%), colorectal, prostate, and stomach. Breast cancer is the fifth most common cause of cancer-related mortality, with lung cancer being the leading cause. In India, breast and cervical cancers are the two most common cancers among women. This study was undertaken to analyse the expression of CD10 in invasive duct cancer (IDC) and its correlation with the various clinicopathological features and hormone status. Materials and methods This study was conducted in the Department of Pathology, Saveetha Medical College and Hospital on 42 cases of invasive ductal carcinoma - no special type (IDC NST). The clinical and histopathologic parameters such as age, tumor site, tumor size, histologic type, histologic grade, lymph node metastases, lymphovascular invasion, and perineural invasion were assessed in hematoxylin and eosin-stained sections of the tumor tissue along with the hormone status of positivity for ER, PR and Her2Neu. These parameters were subsequently compared with the expression of CD10 in the corresponding slides and statsitical correlation was done using the chi square test. Results The most common age group was more than 40 years, with 41-50 years, and 51-60 years in particular. CD10 was positive in 93% of cases. There was a positive correlation between CD 10 expression and lymphovascular invasion in the study (p-0.006). There was no significant relationship between hormone status and CD10 expression. Conclusion A significant association was seen between CD10 expression and lymphovascular invasion. No relation was found between CD10 and the other parameters such as tumour grade, lymph node metastases, lymphovascular invasion, perineural invasion and hormone status. Further studies are required to explore the potential of CD10 as a prognostic marker for IDC.

Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients.

Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients. Two hundred and twelve primary breast cancer patients underwent 18F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P-value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy. A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms. Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.

Metastatic patterns of subcarinal, right and left recurrent laryngeal nerve lymph nodes in thoracic esophageal squamous cell carcinoma without neoadjuvant therapy

PurposeThis research aimed to clarify the metastatic patterns of subcarinal, right and left recurrent laryngeal nerve lymph nodes in thoracic esophageal squamous cell carcinoma and to investigate appropriate strategies for lymph node dissection.MethodsPatients with thoracic esophageal squamous cell carcinoma receiving esophagectomy from December 2020 to April 2024 were retrospectively analyzed. Risk factors for subcarinal, right and left recurrent laryngeal nerve lymph nodes metastasis were determined by chi-square test and multivariate logistic regression analysis. We visualized the metastasis rates of these specific lymph nodes based on the different clinicopathological characteristics. Correlation between subcarinal, right and left recurrent laryngeal lymph nodes metastasis and postoperative complications were also analyzed.ResultsA total of 503 thoracic esophageal squamous carcinoma patients who underwent esophagectomy were enrolled. The metastasis rates of subcarinal, right and left recurrent laryngeal nerve lymph nodes were 10.3%, 10.3%, and 10.9%, respectively. The lymphovascular invasion status and tumor location were the significant predictors for subcarinal and right recurrent laryngeal nerve lymph nodes metastasis, respectively (P < 0.001 and P = 0.013). For left recurrent laryngeal nerve lymph node metastasis, younger age (P = 0.020) and presence of lymphovascular invasion (P = 0.009) were significant risk factors. Additionally, pulmonary infection is the most frequent postoperative complication in patients with dissection of subcarinal, right and left recurrent laryngeal lymph nodes. There was no significant difference in the incidence of anastomotic leakage (P = 0.872), pulmonary infection (P = 0.139), chylothorax (P = 0.702), and hoarseness (P = 0.179) between the subcarinal lymph node dissection cohort and the reservation cohort. The incidence of hoarseness significantly increased in both right (P = 0.042) and left (P = 0.010) recurrent laryngeal nerve lymph nodes dissection cohorts compared by the reservation cohorts, with incidence rates of 5.9% and 6.7%, respectively.ConclusionsThe metastasis rates of subcarinal, right and left recurrent laryngeal nerve lymph nodes in thoracic esophageal squamous cell carcinoma were all over 10%. The dissection of subcarinal lymph nodes does not increase postoperative complications risk, while recurrent laryngeal nerve lymph nodes dissection significantly increases the incidence of hoarseness. Thus, lymph node dissection of subcarinal lymph nodes should be conducted routinely, while recurrent laryngeal nerve lymph nodes dissection may be selectively performed in specific patients.

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

Who should get a right colectomy in low-grade appendiceal adenocarcinomas? Association of lymphovascular invasion and nodal metastases

BackgroundThere is variation in the probability of nodal metastases from low-grade appendiceal adenocarcinomas, and the role of right colectomy is unclear. We aimed to define the prevalence and utility of lymphovascular invasion in predicting the risk of nodal metastases to help stratify patients who may benefit from right hemicolectomy. MethodsPatients with nonmetastatic low-grade appendiceal adenocarcinomas were identified from the National Cancer Database (2010–2017). The primary outcome was probability of nodal metastases. Logistic regression was used to identify independent predictors of nodal metastases. A 4-tier risk model—the COH Composite Score—was calculated by assigning 1 point each for a high-risk feature (lymphovascular invasion, T3/T4 T stage, or nonmucinous histology). Survival analysis was performed using the Kaplan-Meier method. Multivariate Cox regression analysis was used to identify independent predictors of survival. ResultsA total of 1,303 patients with nonmetastatic low-grade appendiceal adenocarcinomas (64.2% mucinous) were identified. Of the 1,133 patients with known lymphovascular invasion status, 78 (6.9%) were lymphovascular invasion positive. In multivariate analysis, lymphovascular invasion was independently associated with nodal metastases (odds ratio, 8.68; P < .001). Overall accuracy of lymphovascular invasion in predicting nodal metastases was 86%. The COH Composite Score stratified patients in 4 categories with increasing risk of nodal metastases and incrementally worse survival. For patients with the COH Composite Score of 0 (12%), the nodal metastasis rate was 3.1%, and a right hemicolectomy in this group did not improve survival. ConclusionThe presence of lymphovascular invasion is strongly predictive of nodal metastases. Lymphovascular invasion as part of the COH Composite Score may help guide the extent of surgery in low-grade appendiceal adenocarcinomas.

A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions

PurposeTo evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models. MethodsThis retrospective study included 262 patients with breast cancer—183 in the training cohort (144 LVI-negative and 39 LVI-positive cases) and 79 in the validation cohort (59 LVI-negative and 20 LVI-positive cases). Radiomics features were extracted from the intra- and peritumoral breast regions using multimodal MRI to generate gross tumor volume (GTV)_radiomics and gross tumor volume plus peritumoral volume (GPTV)_radiomics. Subsequently, DL models (GTV_DL and GPTV_DL) were constructed based on the GTV and GPTV to determine the LVI status. Finally, the most effective radiomics and DL models were integrated with imaging findings to establish a hybrid model, which was converted into a nomogram to quantify the LVI risk. ResultsThe diagnostic efficiency of GPTV_DL was superior to that of GTV_DL (areas under the curve [AUCs], 0.771 and 0.720, respectively). Similarly, GPTV_radiomics outperformed GTV_radiomics (AUC, 0.685 and 0.636, respectively). Univariate and multivariate logistic regression analyses revealed an association between imaging findings, such as MRI-axillary lymph nodes and peritumoral edema (AUC, 0.665). The hybrid model achieved the highest accuracy by combining GPTV_DL, GPTV_radiomics, and imaging findings (AUC, 0.872). ConclusionThe diagnostic efficiency of the GPTV-derived radiomics and DL models surpassed that of the GTV-derived models. Furthermore, the hybrid model, which incorporated GPTV_DL, GPTV_radiomics, and imaging findings, demonstrated the effective determination of LVI status prior to surgery in patients with breast cancer.

Quantitative parameters of dual-layer spectral detector computed tomography for evaluating differentiation grade and lymphovascular and perineural invasion in colorectal adenocarcinoma

PurposeTo explore the predictive value of dual-layer spectral detector CT (SDCT) quantitative parameters for determining differentiation grade, lymphovascular invasion (LVI) and perineural invasion (PNI) in colorectal adenocarcinoma (CRAC) patients. MethodsA total of 106 eligible patients with CRAC were included in this study. Spectral parameters, including CT values at 40 and 100 keV, the effective atomic number (Zeff), the iodine concentration (IC), the slope of the spectral Hounsfield unit (HU) curve (λHU), and the normalized iodine concentration (NIC) in the arterial phase (AP) and venous phase (VP), were compared according to the differentiation grade and the status of LVI and PNI. The diagnostic accuracies of the quantitative parameters with statistical significance were determined via receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated. ResultsThere were 57 males and 49 females aged 43–86 (69 ± 10) years. The measured values of the spectral quantitative parameters of the CRAC were consistent within the observer (ICC range: 0.800–0.926). The 40 keV-AP, IC-AP, NIC-AP, 40 keV-VP, and IC-VP were significantly different among the different differentiation grades in the CRAC (P = 0.040, AUC = 0.673; P = 0.035, AUC = 0.684; P = 0.031, AUC = 0.639; P = 0.044, AUC = 0.663 and P = 0.035, AUC = 0.666, respectively). A statistically significant difference was observed in 40 keV-VP, 100 keV-VP, Zeff-VP, IC-VP, and λHU-VP between LVI-positive and LVI-negative patients (P = 0.003, AUC = 0.688; P = 0.015, AUC = 0.644; P = 0.001, AUC = 0.688; P = 0.001, AUC = 0.703 and P = 0.003, AUC = 0.677, respectively). There were no statistically significant differences in the values of the spectral parameters of the PNI state of patients with CRAC (P > 0.05). ConclusionThe quantitative parameters of SDCT had good diagnostic efficacy in differentiating between different grades and statuses of LVI in patients with CRAC; however, SDCT did not have value for identifying the state of PNI.

Clinical outcomes in salivary adenoid cystic carcinoma.

6102 Background: Adenoid cystic carcinoma (ACC) is a rare salivary gland malignancy with a paucity of clinical outcome data. As such, evidence supporting and informing current guidelines remains limited. Methods: Inclusion criteria stipulated adults with adenoid cystic carcinoma treated at the University of Michigan between 1988 and 2019 with complete records (n=102). Clinical variables including demographics, disease stage, and treatment modality were abstracted. Pathologic variables analyzed included tumor grade, perineural invasion, lymphovascular invasion, and surgical margin status. Multivariate analyses were performed with disease recurrence calculated with Kaplan-Meier methodology as the primary outcome. Results: Mean age at diagnosis was 59 years; stage I and II disease accounted for 35 (38%) patients. Initial treatment was most commonly surgery with adjuvant radiation (77%). Median follow-up was 3 years. Distant metastatic disease developed in 34 (33%) patients. Most common sites included lung (68%), bone (21%), and brain (21%). Median time to distant metastases was 46 months with median overall survival of 58 months. Patients with locoregional recurrence experienced median survival of 91 months. Multivariate analysis adjusted for grade, perineural invasion, and surgical margin status (Table). Grade 3 tumors with predominantly solid histologic pattern pose higher risk of recurrence compared to grade 1 tubular histologic patterns as a reference (HR 3.69, 95% CI 1.15-11.88, p=0.028). Conclusions: Survival among patients with adenoid cystic carcinoma after locoregional recurrence is significantly better compared to those experiencing distant metastasis. Furthermore, tumor grade is most predictive of recurrence. Additionally, distant metastases may emerge over the course of many years. These data from one of the largest retrospective databases to date may inform the development of disease-specific surveillance guidelines and novel salvage treatment paradigms. [Table: see text]

The clinical meaning of lymphovascular invasion: preoperative predictors and postoperative implications in prostate cancer - a retrospective study.

Lymphovascular invasion (LVI) is a pivotal histopathological parameter in prostate cancer (PCa), holding significant prognostic implications. Our study pursued a dual objective: firstly, to identify preoperative factors associated with LVI, aiming to unveil markers facilitating the recognition of patients prone to LVI during postoperative examination; and secondly, to assess postoperative outcomes correlated with LVI. We retrospectively analyzed 861 nonmetastatic PCa patients who underwent radical prostatectomy (RP), investigating preoperative factors and postoperative outcomes. Surgical specimens were processed following established guidelines. Statistical analyses utilized non-parametric tests to assess the association between LVI and both pre- and postoperative factors. Furthermore, logistic regression analyses were utilized to develop models aimed at identifying the most significant predictors of LVI and pN1 status, respectively. Numerous preoperative factors exhibited significant correlations with LVI, offering valuable clinical insights. Logistic regression identified magnetic resonance imaging (MRI)-based clinical tumor stage (cT) 3-4, biopsy Gleason Grading Group (GGG) 3-5, preoperative prostate specific antigen (PSA) ≥20 and percentage of positive biopsy cores (PPBC) ≥50% as the strongest preoperative predictors of LVI. Additionally, the study uncovered an association between LVI and postoperative outcomes, including postoperative PSA (p value <0.001), extracapsular extension (ECE) (<0.001), positive surgical margins (PSM) (<0.001), perineural invasion (PNI) (<0.001), pathological tumor stage (pT) (<0.001), pathological lymph node status (pN) (<0.001), postoperative GGG (<0.001), and operative time (0.023). Notably, the study revealed a novel and substantial association between LVI and an increased number of positive lymph nodes in pN+ patients in the univariate analysis (<0.001). Furthermore, we have found an association between LVI and pN1 status in the logistic regression analysis (odds ratio [OR] = 23.905; p <0.001). Our findings underscore the pivotal role of LVI in influencing the prognosis of prostate cancer (PCa). The study acknowledges the challenges associated with preoperative LVI assessment and emphasizes the need for future research to unravel the factors associated with this histopathological finding. Significantly, our research stands out as the first, to the best of our knowledge, toreveal the association between LVI and the number of positive lymph nodes in pN+ patients.

Abstract PO1-26-01: Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer

Abstract Background: Patient-derived xenografts (PDXs) are pivotal in cancer research. Despite histopathological insights into factors driving PDX success, the role of artificial intelligence (AI) in predicting PDX engraftment remains unexplored. We aimed to bridge this gap by integrating clinicopathological data and AI-based morphometric analysis to predict PDX success in breast cancer. Methods: PDXs were generated from tumor tissues derived from breast cancer patients who had undergone surgical intervention. Clinicopathological information including subtypes, pathological diagnosis, modified Bloom-Richardson system histologic grades, treatment with neoadjuvant chemotherapy (NAC), Miller Payne grade, residual caner burden score, invasive tumor size, lymphovascular invasion status, AJCC 8th T and N stages and the percentage of tumor infiltrating lymphocytes were collected and analyzed. For the image analysis component, whole-slide images (WSIs) of hematoxylin and eosin–stained tissue samples from 64 surgically resected breast cancer patients were used as a training set for an AI model under the supervision of 2 pathologists to extract morphometric features. The model transformed image tiles into patches of morphologically similar patterns, and categorized them into adipose tissue, background, necrosis, invasive carcinoma, carcinoma in situ, stroma, and terminal ductal lobular unit. This trained model was subsequently applied to the WSIs employed in the establishment of PDXs. The classified patches and their relative ratios within the invasive tumor boundary were compiled. The consolidated data from clinicopathological and image analyses were subjected to logistic regression to discern correlates of successful PDX engraftment. Results: Out of the 311 patient tumor samples used for generating PDXs, (131 post-chemotherapy, 180 chemo-naïve), 47 PDXs were successfully established (15.1%). Logistic regression revealed several factors for successful engraftment including NAC treatment with an odds ratio of 6.71 (28.2% vs 5.6%, 95% CI: 2.53 - 17.80, p &amp;lt; 0.001), higher histologic grades (25.3% vs 2.2%, OR = 5.80, 95% CI: 1.49 - 22.63, p = 0.01), triple negative breast cancer compared to hormone receptor-positive cancers (32.0% vs 3.3%, OR = 10.99, 95% CI: 1.26 - 95.89, p = 0.03), and tumors of larger size (OR = 1.34, 95% CI: 1.02 - 1.76, p = 0.03). Interestingly, the percentage of specific tissue patch types within tumor did not significantly impact the likelihood of successful PDX engraftment. However, in our logistic regression analysis based solely on morphometric features, presence of necrosis within the tumor notably enhanced PDX establishment. Specifically, each percent increase in necrosis within tumor boosted the odds of successful PDX creation by 0.01% (OR = 1.0001, 95% CI: 1.00003 - 1.00024, p = 0.01). Conclusions: PDXs are often successfully established from clinically aggressive breast cancers, particularly those with NAC treatment, higher histologic grades, TNBC subtype, and larger tumor size. While morphometric features contribute to the prediction of PDX engraftment success, their importance is surpassed by these clinicopathological factors. However, presence of necrosis emerged as a key morphometric predictor of successful PDX engraftment. Keywords: breast cancer; patient-derived xenograft (PDX); Breast Cancer Morphometrics; Cancer Predictive Modeling. Citation Format: Jongwon Lee, GeonHee Lee, Gyungyub Gong, Hee Jin Lee. Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-01.

Abstract PO1-07-11: Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets

Abstract Background: Intratumor heterogeneity (ITH) refers to variations observed among cancer cells within a single tumor, posing significant challenges in clinical practice due to its contribution to treatment resistance and unfavorable outcome. However, the inconvenience of multi-region biopsy and limitations of genomic sequencing based on limited tissue impede the practical detection of ITH. Consequently, there is an urgent need for a non-invasive method that comprehensively captures ITH from a holistic perspective of the entire tumor. Methods: We utilized a large multicenter dataset comprising dynamic contrast-enhanced magnetic resonance images from breast cancer patients (n = 1423) along with matched multiomics data (n = 468) to develop a non-invasive machine learning approach for measuring ITH. We extracted quantitative radiomic features from both the entire tumor and peritumor regions, with a specific focus on features associated with imaging heterogeneity. Using these radiomic features, we established an imaging ITH (IITH) model. The robustness of IITH evaluation was determined by assessing its correlation with genomic ITH (employing the mutant-allele tumor heterogeneity [MATH] algorithm) and pathological cellular ITH (analyzing variation in quantitative nuclear features extracted through digital pathology). Prognostic power was evaluated using Kaplan-Meier and multivariate Cox proportional hazards analyses. Integrated multiomics analyses were performed to investigate the molecular basis of different IITH subgroups. Results: We developed a non-invasive radiomic signature to quantify imaging ITH (IITH) in the FUSCC cohort. Breast cancer patients were retrospectively categorized into high and low IITH groups. Multivariate Cox analysis identified high IITH as an independent predictor of poor prognosis in breast cancer patients, even after adjusting for clinical risk factors such as tumor size, positive lymph nodes, clinical subtype and lymphovascular invasion status. These findings were further validated in the DUKE cohort, confirming the prognostic value of IITH. Moreover, we substantiated the robustness of IITH by demonstrating its association with genomic and pathological ITH. Multiomics analysis revealed the activation of oncogenic pathways and metabolic dysregulation in high-IITH tumors. Intriguingly, our investigation also highlighted ferroptosis as a vulnerability and potential therapeutic target in high-IITH tumors, which was further supported by evidence from the TCGA cohort. Conclusion: Radiomic-based assessment of ITH provides a non-invasive approach to comprehensively capture ITH and predict the prognosis of breast cancer patients. Targeting ferroptosis may hold promise as a treatment strategy for patients with high IITH. Citation Format: Guan-Hua Su, Yi Xiao, Chao You, Ren-Cheng Zheng, Shen Zhao, He Wang, Yi-Zhou Jiang, Ya-Jia Gu, Zhi-Ming Shao. Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-11.

Lympho-vascular invasion in oral cavity cancers – A bridge between tumour biology and clinical outcomes

ObjectiveVarious histo-pathological prognostic factors have been identified in oral cavity squamous cell carcinomas (OCSCC). One such factor is the status of lympho-vascular invasion (LVI). We carried out this analysis to evaluate the association of LVI with other prognostic factors and the clinical outcomes. MethodsData of patients with OCSCC who underwent curative intent surgery from January-2017 to December-2019 at our institute were retrospectively evaluated. The association of LVI with various tumour-node related prognostic factors and its effect on overall and disease-free survival (OS, DFS) were analyzed. Resultsof the total 234 patients, 71 % (166) had Bucco-alveolar-complex primary. 62.8 % (147) had locoregionally advanced disease. 91.5 % (214) required adjuvant treatment.The LVI positivity was 77.4 %, 63.3 % in tumour size >3cm/<3 cm, 85 %, 59.7 % in depth of infiltration >1cm/<1 cm, 82.4 %, 57.6 % in T3-T4/T1-2, 91.9 %, 59.9 % in grade 3/others and 96.8 %, 13.8 % in perineural invasion present/absent, respectively (p < 0.05). In the presence of LVI, lymph node positivity was 60.6 %, N2–N3 were 41.9 % and extra-capsular extension was 26.9 % as compared to LVI negative – 5.4 %, 5.4 % and 4.1 % respectively (p < 0.05).With median follow up of 37.3 months, 2year OS and DFS were 76.6 % and 75.5 % respectively. The 2year OS and DFS for the LVI-positive group were 70.4 % and 69.3 % compared to 90.6 % and 89.3 % for LVI-negative (p = 0.00). Loco-regional and distal (any) recurrence was seen in 20.9 % (49) and 7.3 % (17), the same for LVI-positive was 25.6 % (41) and 10 % (16) compared to 10.8 % (8) and 1.4 % (1) for LVI-negative (p < 0.05). ConclusionIn oral cavity cancers, there is positive association of LVI with various tumour-node related prognostic factors and clinical outcomes.