Lymphovascular Invasion Status Research Articles

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.

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study

Rationale and ObjectivesThe aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). Materials and MethodsIn this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. ResultsThe DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. ConclusionThe DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer

ObjectivesLymphovascular invasion serves as a crucial prognostic indicator in invasive breast cancer, influencing treatment decisions. We aimed to develop a machine learning model utilizing optimal volumes of interest extracted from multisequence magnetic resonance images to predict lymphovascular invasion in patients with invasive breast cancer. Materials and methodsThis study comprised 191 patients postoperatively diagnosed with invasive breast cancer through multi-sequence magnetic resonance imaging. Independent predictors were identified through univariate and multivariate logistic regression analyses, culminating in the construction of a clinical model. Radiomic features were extracted from multi-sequence magnetic resonance imaging images across various volume of interest scales (−2 mm, entire, +2 mm, +4 mm, and +6 mm). Subsequently, various radiomic models were developed using machine learning model algorithms, including logistic regression, support vector machine, k-nearest neighbor, gradient boosting machine, classification and regression tree, and random forest. A hybrid model was then formulated, amalgamating optimal radiomic and clinical models. ResultsThe area under the curve of the clinical model was 0.757. Among the radiomic models, the most efficient diagnosis was achieved by the k-nearest neighbor-based radiomics-volume of interest (+2 mm), resulting in an area under the curve of 0.780. The hybrid model, integrating the k-nearest neighbor-based radiomics-volume of interest (+2 mm), and the clinical model surpassed the individual clinical and radiomics models, exhibiting a superior area under the curve of 0.864. ConclusionUtilizing a hybrid approach integrating clinical data and multi-sequence magnetic resonance imaging-derived radiomics models based on the multiscale tumor region volume of interest (+2 mm) proved effective in determining lymphovascular invasion status in patients with invasive breast cancer. This innovative methodology may offer valuable insights for treatment planning and disease management.

An insight into immunohistochemical profile of triple negative breast carcinoma in a tertiary care center

Introduction: Triple Negative Breast Cancer (TNBC) is a type of breast cancer that fails to express Estrogen Receptor (ER), Progesterone Receptor (PR) and Human Epidermal Growth Factor Receptor 2 (HER2) in Immunohistochemical analysis. TNBC has a unique molecular profile, aggressive nature and lack of efficient targeted therapies. The aim of the study was to evaluate the expression of basal markers like Epidermal Growth Factor Receptor (EGFR) and Cytokeratin 5/6 (CK 5/6) and to correlate their expression with the clinico-pathological parameters, thus enabling the identification of the aggressive basal like phenotype of TNBC in resource-constrained settings. Methodology: The study was conducted at a South Indian tertiary care centre for 2 years. Data such as age of presentation, tumour measurements, margins and necrosis, numbers, levels and sizes of lymph nodes were collected. Data on histological type and grade, in situ component, lymphovascular and perineural invasion, stromal reaction and lymph node status were recorded. Primary Immunohistochemical studies were performed for the markers ER,PR and HER2.Patients with negative expression for all the three markers were identified. IHC studies for EGFR and Cytokeratin 5/6(CK5/6) were done on tissue blocks and the expression of these two markers were equated with the clinico-pathological parameters. Results: Among the 94 cases, 44 cases (46.8%) expressed CK5/6 and 46 (48.9%) expressed EGFR. A plausible association was noted between the expression of the two markers with age of the patient, tumour size, histological grade and tumour necrosis. Conclusion: Combination of the time honoured gross and histopathological examinations along with the application of basal markers like CK 5/6 and EGFR, helps in identifying the basal-like phenotype with dismal clinical outcome among the triple-negative breast cancers in resource-constrained settings. An insight into such an aggressive tumour, provided by these markers may be critical in designing personalized treatment strategies for such patients.

Abstract 961: Detection of minimal residual disease in lymph predicts recurrence in HPV-negative head and neck cancer patients

Abstract Background: Locoregional cancer relapse remains a major cause of failure in head and neck squamous cell carcinoma (HNSCC), particularly for HPV-negative patients whose 2-year locoregional failure rate is up to 50%. There is unmet need for an accurate diagnostic test that predicts risk of recurrence prior to adjuvant therapy selection. We present a novel proximal assay for minimal residual disease (MRD) profiled in lymphatic exudate collected via surgical drains (“lymph”). Methods: Lymph, plasma, and blood were collected from 46 HPV-negative HNSCC patients postoperatively at 24 hours along with resected tumor. Cell-free DNA was extracted from lymph and plasma and sequenced using the TruSeq Oncology 500 panel to a depth of &amp;gt;100 million reads. Somatic mutations were identified by exome sequencing (200x) tumor and blood. Five patients had &amp;lt;2 mutations in tumor and were excluded. Two patients were censored due to lack of clinical data, yielding 18 patients with disease recurrence (REC) and 21 with no evidence of disease (NED) with &amp;gt;1 year of follow-up. Tumor-specific variants were force-called in lymph and plasma using a custom pipeline. Patients were considered MRD positive if the mean variant allele fraction (mVAF) was greater than 0.015% (the estimated limit of detection). The Kaplan-Meier (KM) estimator with log-rank test and Cox proportional-hazards model were used for survival analyses. Logistic regression model was performed with 5-fold cross-validation. Results: KM survival analyses showed lymph accurately predicts recurrence (sensitivity (SN) = 78%, specificity (SP) = 67%; p = 0.003, Hazard ratio (HR) = 4.7), while plasma was not significant (p = 0.92) at this postoperative timepoint. Performance was enhanced for locoregional relapse (SN = 92%, SP = 67%; p = 0.001, HR = 13.9. N=33). We also observed accurate recurrence prediction (SN = 83%, SP = 69%, p = 0.03, HR = 7.7) in the 19 N0 patients (node-negative by pathology). Lymph MRD outperformed individual pathology features (extranodal extension, perineural invasion, lymphovascular invasion, and nodal disease status) for recurrence prediction (HR = 3.4, 0.88, 2.5, 2.4, respectively). A logistic regression model combining lymph MRD with these 4 high-risk pathologic features showed superior performance over lymph alone or pathology alone (SN = 89%, SP = 67%; p = 0.0007, HR = 8.6). Conclusions: ctDNA analysis of lymph from surgical drains represents a novel proximal MRD approach in HPV-negative HNSCC. Postoperative lymph significantly outperforms plasma for prediction of recurrence, particularly in patients with locoregional relapse. Accurate MRD identification in patients with lower risk pathologic features suggests that lymph MRD testing has potential to augment traditional pathology and provide more personalized adjuvant treatment decision-making in patients with HPV-negative HNSCC. Citation Format: Wendy Winckler, Zhuosheng Gu, Damion Whitfield, Noah Earland, Adam Harmon, Megan Long, Peter Harris, Zhongping Xu, Ricardo Ramirez, Sophie Gerndt, Maciej Pacula, Marra S. Francis, Jose P. Zevallos, Aadel A. Chaudhuri. Detection of minimal residual disease in lymph predicts recurrence in HPV-negative head and neck cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 961.

Prediction of Lymphovascular invasion status in breast cancer based on magnetic resonance imaging radiomics features

ObjectiveThis study intended to investigate the feasibility and effectiveness of using clinical magnetic resonance imaging (MRI) radiomics features to predict lymphovascular invasion (LVI) status in breast cancer (BC) patients. MethodsA total of 182 BC patients were retrospectively collected and randomly divided into a training set (n = 127) and a validation set (n = 55) in a 7:3 ratio. Based on pathological examination results, the training set was further divided into LVI group (n = 60) and non-LVI group (n = 67), and the validation set was divided into LVI group (n = 24) and non-LVI group (n = 31). General data and MRI examination indicators were compared. Multivariate logistic regression was utilized to analyze MRI radiomics features and clinically relevant indicators that were significant in the baseline information of patients in training set, independent risk factors were identified, and a logistic regression model was built. The accuracy of logistic model was validated using ROC curves in training and validation sets. ResultsAge, pathohistological classification, tumor length, tumor width, presence or absence of Magnetic Resonance Spectroscopy (MRS) cho peak, presence or absence of spicule sign, peritumoral enhancement, and peritumoral edema were statistically significant (P < 0.05) between the two groups. Multivariate logistic regression analysis presented that spicule and peritumoral edema were independent risk factors for LVI in BC patients (P < 0.05). The ROC curve illustrated that AUC of the logistic regression model in the training set was 0.807 (95%CI: 0.730–0.885) and that in the validation set was 0.837 (95%CI: 0.731–0.944). ConclusionRadiomics features of spicule sign and peritumoral edema were independent risk factors for LVI in BC patients. A logistic regression model based on these factors, along with age, could accurately predict LVI occurrence in BC patients, providing data support for diagnosis and modeling of LVI in BC patients.

Site-Specific Differences of Eligibility for Adjuvant Immunotherapy Among Urothelial Carcinoma Patients Treated With Radical Surgery: Results From a Multicenter Cohort Study

BackgroundThe CheckMate274 trial has reported enhanced disease-free survival rates in patients with stage pT3–4/ypT2–4 or pN+ urothelial carcinoma (UC) undergoing adjuvant nivolumab therapy. This study compares prognostic differences between urothelial carcinoma of the bladder (UCB) and upper tract urothelial carcinoma (UTUC). MethodsWe retrospectively analyzed data from 719 patients with UC who underwent radical surgery, stratifying to patients at stage pT3–4 and/or pN+ without neoadjuvant chemotherapy (NAC) or at ypT2–4 and/or ypN+ with NAC (potential candidates for adjuvant immunotherapy), and to those who were not candidates for adjuvant immunotherapy. We used Kaplan–Meier curves to assess oncological outcomes, particularly nonurothelial tract recurrence-free survival (NUTRFS), cancer-specific survival (CSS), and overall survival (OS). Risk factors were identified by Cox regression analysis. ResultsKaplan–Meier curves showed significantly lower NUTRFS, CSS, and OS for potential adjuvant immunotherapy candidates than for noncandidates in each UCB and UTUC group. NUTRFS, CSS, and OS did not differ significantly between adjuvant immunotherapy candidates with UBC or UTUC. Trends were similar among patients ineligible for adjuvant immunotherapy. Pathological T stage (pT3–4 or ypT2–4), pathological N stage, and lymphovascular invasion (LVI) were independent predictors of oncological outcomes on multivariate analysis. ConclusionThe criteria for adjuvant immunotherapy candidates from the CheckMate 274 trial can also effectively stratify UC patients after radical surgery. Substantial clinical significance is attached to LVI status as well as to pathological T and N status, suggesting that LVI status should be considered when selecting suitable candidates for adjuvant immunotherapy.

Methodology of ICG marking technique in modern pelvic surgery: applied aspects (Literature review)

The article is devoted to a review of scientific publications on the use of the Indocyanine green (ICG) labeling technique in modern pelvic surgery. This dye was developed by Kodak Research Laboratories in 1955 and approved for clinical use in 1959, but has only recently come into widespread use. ICG-enhanced near-infrared fluorescence is introduced to improve visualization and provide more detailed anatomical localization during surgery.The dye binds to blood plasma proteins, it is quickly excreted from the organism and is non-toxic and has no found complications after its administration. Many studies have been conducted on the use of this technique: visualization of sentinel lymph nodes (SLN) in various oncogynecological diseases; visualization of ureters, lymphatic drainage, tissue perfusion, vessels of various sizes, colorectal blood circulation, etc. The high detection rate of SLN (unilateral and bilateral) with ICG compared with other dyes such as radiocolloid and patent blue suggests its effectiveness.The condition of SLN has an important prognostic and therapeutic value, based on the obtained results, a more correct further adjuvant treatment can be prescribed. A sentinel lymph node labeling procedure was performed as an alternative to systemic lymphadenectomy to assess the status of lymphovascular invasion. The use of ICG-marking allows to reduce the risk of intraoperative and postoperative complications. Many experimental studies have been conducted in relation to the assessment of uterine vascularization, preservation of individual vessels, their identification and measurement of blood flow.The article provides data from scientists on the use of the ICG-marking technique in endometrial, cervical, vulva, vaginal, ovarian cancer, as well as in endometriosis and for visualization of the ureters. This technique causes active discussions in the scientific community, requires further research and should be more widely used in various directions of surgical interventions.

Prognostic factors and survival of breast cancer in patients over 40 years of age

Aims: Breast cancer is the cancer with the highest incidence and mortality in women. There are differences in prognosis and survival between women over and under the age of 40. In this article, we aimed to examine breast cancer prognostic factors and survival results in people over the age of 40. Methods: 1187 patients aged 40 and over who underwent surgery at the Ondokuz Mayıs University Department of General Surgery between August 2005 and April 2019 and whose data were accessible were retrospectively examined. Data were obtained from the hospital automation system, the Ministry of Health’s online database, hospital archives, patients, and/or their relatives. They were classified separately in terms of type of surgery, axillary metastasis status (according to radiological status if axilla surgery is not performed), type of axilla surgery performed, pathological tumor size, number of pathological lymph nodes, pathological stage, lymphovascular and perineural invasion status, hormone receptor positivity, C-erb B2 and Ki-67 status, neoadjuvant treatment status, and molecular subgroup. Variables were analyzed individually for recurrence, mortality, and survival. Results found to be significant were subjected to multivariate analysis testing. Statistical significance was accepted as p&lt;0.05. Results: As a result of multivariate analysis performed by excluding data that disrupted homogeneous distribution, perineural invasion, lymphovascular invasion, grade, and progesterone receptor status were determined to be independent prognostic factors in terms of recurrence. Lymphovascular invasion and progesterone receptor status were found to be independent prognostic factors for mortality. Conclusion: A lot of studies have been conducted, and criteria have been determined for breast cancer prognosis and survival. In our results, lymphovascular invasion and progesterone receptor status were found to be independent prognostic markers for both recurrence and mortality. More reliable results can be obtained with prospective study analyses.

Prognostic Implication of Lymphovascular Invasion in Early Gastric Cancer Meeting Endoscopic Submucosal Dissection Criteria: Insights from Radical Surgery Outcomes.

The management of early gastric cancer (EGC) has witnessed a rise in the utilization of endoscopic submucosal dissection (ESD) as a treatment modality, although prognostic markers are needed to guide management strategies. This study investigates the prognostic implications of lymphovascular invasion (LVI) in ESD-eligible EGC patients, specifically its implications for subsequent radical surgery. A retrospective, multicenter study from two primary hospitals analyzed clinicopathological data from 1369 EGC patients eligible for ESD, who underwent gastrectomy at Shanghai Cancer Center and Huashan Hospital between 2009 and 2018. We evaluated the relationship between LVI and lymph node metastasis (LNM), as well as the influence of LVI on recurrence-free survival (RFS) and overall survival (OS). We found a strong association between LVI and LNM (p < 0.001). Advanced machine learning approaches, including Random Forest, Gradient Boosting Machine, and eXtreme Gradient Boosting, confirmed the pivotal role of LVI in forecasting LNM from both centers. Multivariate analysis identified LVI as an independent negative prognostic factor for both RFS and OS, with hazard ratios of 4.5 (95% CI: 2.4-8.5, p < 0.001) and 4.4 (95% CI: 2.1-8.9, p < 0.001), respectively. LVI is crucial for risk stratification in ESD-eligible EGC patients, underscoring the necessity for radical gastrectomy. Future research should explore the potential incorporation of LVI status into existing TNM staging systems and novel therapeutic strategies.

The impact of squamous cell transformation on the prognosis of patients treated with radical nephroureterectomy

Background Limited information is available for guiding the management of upper urinary tract (UUT) urothelial carcinoma with squamous differentiation (UC-SqD). We did not even know about the difference between pure urothelial carcinoma (UC) and UC-SqD in the UUT regardless of treatment policy and prognosis. Instead of direct comparisons against each other, we included the third UUT malignancy, squamous cell carcinoma (SCC). This three-way-race model allows us to more clearly demonstrate the impact of squamous cell transformation on patient outcomes in UUT malignancy.MethodsWe retrospectively analysed 327 patients with UC, UC-SqD, or SCC who underwent radical nephroureterectomy with bladder cuff excision (RNU) at Taichung Veterans General Hospital, Taichung, Taiwan, between January 2006 and December 2013. A Kaplan–Meier survival analysis was used to evaluate the relationship between patient outcomes and histology. Multivariate Cox proportional hazards modelling was also used to predict patient prognoses.ResultsThe five-year postoperative cancer-specific survival (CSS) rates were 83.6% (UC), 74.4% (UC-SqD), and 55.6% (SCC), and the 5-year recurrence-free survival (RFS) rates were 87.7% (UC), 61.5% (UC-SqD), and 51.9% (SCC). UC patients had significantly better 5-year RFS than UC-SqD and SCC patients (P = 0.001 and P < 0.0001, respectively). Patients with pure UC had significantly better 5-year CSS than SCC patients (P = 0.0045). SCC or UC-SqD did not independently predict disease-specific mortality (HR 0.999, p = 0.999; HR 0.775, p = 0.632, respectively) or disease recurrence compared to pure UC (HR 2.934, p = 0.239; HR 1.422, p = 0.525, respectively). Age, lymphovascular invasion (LVI), and lymph node (LN) status independently predicted CSS, while pathological tumour stage, LN status, and LVI predicted RFS.ConclusionsSCC and UC-SqD are not independent predictors of survival outcomes in patients with UUT tumours. However, they are associated with other worse prognostic factors. Hence, different treatments are needed for these two conditions, especially for SCC.

Predicting the status of lymphovascular space invasion using quantitative parameters from synthetic MRI in cervical squamous cell carcinoma without lymphatic metastasis.

To investigate the value of quantitative longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) maps derived from synthetic magnetic resonance imaging (MRI) for evaluating the status of lymphovascular space invasion (LVSI) in cervical squamous cell carcinoma (CSCC) without lymph node metastasis (LNM). Patients with suspected cervical cancer who visited our hospital from May 2020 to March 2023 were collected. All patients underwent preoperative MRI, including routine sequences and synthetic MRI. Patients with pathologically confirmed CSCC without lymphatic metastasis were included in this study. The subjects were divided into negative- and positive-LVSI groups based on the status of LVSI. Quantitative parameters of T1, T2, and PD values derived from synthetic MRI were compared between the two groups using independent samples t-test. Receiver operating characteristic curves were used to determine the diagnostic efficacy of the parameters. 59 patients were enrolled in this study and were classified as positive (n = 32) and negative LVSI groups (n = 27). T1 and T2 values showed significant differences in differentiating negative-LVSI from positive-LVSI CSCC (1307.39 ± 122.02 vs. 1193.03 ± 107.86, P<0.0001; 88.42 ± 7.24 vs. 80.99 ± 5.50, P<0.0001, respectively). The area under the curve (AUC) for T1, T2 values and a combination of T1 and T2 values were 0.756, 0.799, 0.834 respectively, and there is no statistically significant difference in the diagnostic efficacy between individual and combined diagnosis of each parameter. Quantitative parameters derived from synthetic MRI can be used to evaluate the LVSI status in patients with CSCC without LNM.

The Use of Gene Expression Profiling and Biomarkers in Melanoma Diagnosis and Predicting Recurrence: Implications for Surveillance and Treatment

Cutaneous melanoma is becoming more prevalent in the United States and has the highest mortality among cutaneous malignancies. The majority of melanomas are diagnosed at an early stage and, as such, survival is generally favorable. However, there remains prognostic uncertainty among subsets of early- and intermediate-stage melanoma patients, some of whom go on to develop advanced disease while others remain disease-free. Melanoma gene expression profiling (GEP) has evolved with the notion to help bridge this gap and identify higher- or lower-risk patients to better tailor treatment and surveillance protocols. These tests seek to prognosticate melanomas independently of established AJCC 8 cancer staging and clinicopathologic features (sex, age, primary tumor location, thickness, ulceration, mitotic rate, lymphovascular invasion, microsatellites, and/or SLNB status). While there is a significant opportunity to improve the accuracy of melanoma prognostication and diagnosis, it is equally important to understand the current landscape of molecular profiling for melanoma treatment. Society guidelines currently do not recommend molecular testing outside of clinical trials for melanoma clinical decision making, citing insufficient high-quality evidence guiding indications for the testing and interpretation of results. The goal of this chapter is to review the available literature for GEP testing for melanoma diagnosis and prognostication and understand their place in current treatment paradigms.

Impact of lymphovascular invasion on survival in surgically treated upper tract urothelial carcinoma: a nationwide analysis.

To assess the prognostic ability of lymphovascular invasion (LVI) in upper tract urothelial carcinoma (UTUC) as a predictor of overall survival (OS) using a large North American cohort. Our cohort included 5940 patients with clinical M0 UTUC who underwent a radical nephroureterectomy (RNU), between 2010 and 2016, within the National Cancer Database. The main variable of interest was LVI status, and its interaction with pathological nodal (pN) status. Kaplan-Meier curves were used to depict the OS also stratifying patients on LVI status. Cox regression analysis tested the impact of LVI status on OS after accounting for the available covariates. The median (interquartile range [IQR]) age at diagnosis was 71 (63-78) years and most patients had pathological T1 stage disease (48.6%). Nodal status was pN0, pN1 and pNx in 45.8%, 6.3% and 47.9%, respectively. Overall, 22.1% had LVI. The median (IQR) follow-up time was 32.6 (16.0-53.3) months. At the 5-year postoperative follow-up, the estimated OS rate was 28% in patients with LVI vs 66% in those without LVI (P < 0.001). When patients were stratified based on nodal status those rates were 32% vs 68% in pN0 patients (P < 0.001), 23% vs 30% in pN1 patients (P = 0.8), and 28% vs 65% in pNx patients (P < 0.001). On multivariable analysis, the presence of LVI was associated with less favourable OS (hazard ratio 1.79, 95% confidence interval 1.60-1.99; P < 0.001). Our study assessed the impact of LVI on OS in patients with UTUC in a large North American nationwide cohort. Our series, as the largest to date, indicate that LVI is associated with less favourable survival outcomes in patients with UTUC after RNU, and this variable could be used in counselling patients about their prognosis and might be a useful tool for future trials to risk-stratify patients.

The expression of Plakoglobin in residual cancer after neoadjuvant chemotherapy for breast cancer and its prognostic impact on patients

Objective: To investigate the relationship between the expression levels of Plakoglobin protein in residual lesions after neoadjuvant chemotherapy (NAC) and the prognosis of breast cancer patients. Methods: Clinical and pathological data from 174 breast cancer patients who underwent surgery after receiving NAC at the Cancer Hospital of Chinese Academy of Medical Sciences from January 2009 to December 2017 were collected. The expression level of Plakoglobin in residual cancer lesions was evaluated by immunohistochemistry. The correlation between Plakoglobin expression level and clinicopathological features was analyzed. Survival analysis was performed using the Kaplan-Meier method, and Cox proportional hazard regression models were used for factor analysis. Results: Among the 174 patients, 140 had low expression of Plakoglobin, and 34 had high expression. The median disease-free survival (DFS) and overall survival (OS) in the Plakoglobin low expression group were 59.46 and 71.68 months, respectively, both of which were higher than those in the high expression group (36.58 and 47.26 months, respectively, both P<0.05). Univariate analysis showed that Plakoglobin expression, pathological N stage, lymphovascular invasion status, histological grade, Ki-67, and molecular subtypes were associated with OS (all P<0.05), while pathological N stage, histological grade, and Ki-67 were associated with DFS (all P<0.05). Multivariate analysis revealed that Plakoglobin expression (HR=2.438, 95% CI: 1.256-4.735, P=0.008) was an independent predictor for OS, and Ki-67 (HR=2.228, 95% CI: 1.316-3.773, P=0.003) was an independent predictor for DFS. Conclusion: In breast cancer patients with residual lesions after NAC, those with low Plakoglobin expression have relatively longer OS and Plakoglobin is an independent prognostic factor for OS.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

ObjectiveThis study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). MethodsThis study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018–July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. ResultsA total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555–0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. ConclusionThe combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.

Preoperative Dynamic Contrast-Enhanced and Diffusion-Weighted Breast Magnetic Resonance Imaging Findings for Prediction of Lymphovascular Invasion of the Lesions in Node-Negative Invasive Breast Cancer.

Purpose: Our single-centre retrospective study aimed to investigate the relationship between preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings and apparent diffusion coefficient (ADC) values and lymphovascular invasion (LVI) status of the lesions in patients with clinically-radiologically lymph node-negative invasive breast cancer. Methods: A total of 250 breast lesions diagnosed in preoperative magnetic resonance imaging were identified. All patients were divided into 2 subgroups: LVI-negative and LVI-positive according to the pathological findings of surgical specimens. The 2 groups' DCE-MRI findings, ADC values, and histopathological results of lesions were compared. Results: LVI was detected in 100 of 250 lesions. Younger age than 45 years and larger lesion size than 20 mm were found to be associated with the presence of LVI (P < .001). High histological and nuclear grade (P = .001), HER2-enriched molecular subtype (P = .001), and Ki-67 positivity (P = .016) were significantly associated with LVI. The LVI positivity rate was significantly higher in the lesions with medium-rapid initial phase kinetic curve and washout delayed phase kinetic curve (P = .001). The presence of LVI was significantly associated with the presence of peritumoural edema, sentinel lymph node metastasis, adjacent vessel sign, and increased whole breast vascularity (P < .001). When diffusion-weighted imaging findings were evaluated, it was determined that tumoural ADC values lower than 1068 × 10-6 mm2/second (P = .002) and peritumoural-tumoural ADC ratios higher than 1.5 (P = .001) statistically increased the probability of LVI. Conclusion: The patient's age, various histopathological and DCE-MRI findings, tumoural ADC value, and peritumoural-tumoural ADC ratio may be useful in the preoperative prediction of LVI status in breast cancer lesions.

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer

Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions. Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI. Radiomic features were extracted from pre-contrast (A0), first post-contrast (A1), and subtracted images of A0 and A1. Three radiomics models were developed using several data analyses; logistic analyses were performed to identify radiological features to predict the LVI status. A hybrid model integrating both radiological features and optimal radiomics was developed. Receiver operating characteristic analysis was employed to evaluate model performance, using the area under the curve (AUC) as a quantitative metric for discriminative ability. In the test set, the Radiomics-Delta model, with 17 radiomic features, had an AUC of 0.781 and accuracy of 0.705. Radiomics-A0, with 10 features, had an AUC of 0.619 and accuracy of 0.523, while Radiomics-A1, with 8 features, had an AUC of 0.715 and accuracy of 0.591. The hybrid model exhibited better performance, with an AUC of 0.868 and accuracy of 0.875, than the radiological and Radiomics-Delta models, with an AUC of 0.759 and 0.781, respectively, and accuracy of 0.773 and 0.705, respectively. Compared to Radiomics-A0 and Radiomics-A1, Radiomics-Delta demonstrated superior performance. Moreover, the hybrid model incorporating Radiomics-Delta and radiological features exhibited excellent performance in determining the LVI status in cases of invasive breast cancer.