Lymphovascular Invasion in Colorectal Cancer

Background Urothelial bladder carcinoma (UBC) is a chemo-sensitive tumour, but the response to treatment is heterogeneous. CD147 has been associated with chemotherapy resistance. We aimed to define tumours with an aggressive phenotype by the combined analysis of clinicopathological and biological parameters.Methods 77 patients with T1G3 or muscle-invasive UBC treated by radical cystectomy were studied. Immunohistochemistry was performed to detect CD147, heparanase, CD31 (blood vessels identification) and D2-40 (lymphatic vessels identification) expressions. The immunohistochemical reactions were correlated with the clinicopathological and the outcome parameters. 5-year disease free survival (DFS) and overall survival (OS) rates were estimated using the Kaplan-Meier method. Multivariate analysis was performed by Cox proportional hazards analysis. ResultsThe 5-year DFS and OS rates were significantly influenced by the classical clinicopathological parameters, and by the occurrence of lymphovascular invasion.CD147 and heparanase immunoexpression did not affect patients' outcome. However, patients with pT3/pT4 tumours had a median OS time of 14.7 months (95% CI 7.1-22.3, p=0.003), which was reduced to 9.2 months (95% CI 1.5-17.0, p=0.008) if the tumours were CD147 positive. We developed a model of tumour aggressiveness using parameters as stage, grade, lymphovascular invasion and CD147 immunoexpression, which separated a low aggressiveness from a high aggressiveness group, remaining as an independent prognostic factor of DFS (HR 3.746; p=0.019) and OS (HR 3.247; 95% CI 1.015-10.388, p=0.047).Conclusion CD147 overexpression, included in a model of UBC aggressiveness, may help surgeons to identify patients who could benefit from a personalized therapeutic regimen. Additional validation is needed.

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“…Lymphovascular invasion has been suggested as a prognostic factor in several malignancies, including bladder cancer [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

CD147 overexpression allows an accurate discrimination of bladder cancer patients’ prognosis

Afonso

Longatto‐Filho

Baltazar

et al. 2011

European Journal of Surgical Oncology (EJSO)

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“…Immunocytochecmical staining for D2-40 for lymphatic vessels and CD 31 for venous vessels did not improve for identifying lymphovascular invasion using the currently available antibodies [27]. This study highlights the need for criteria in evaluation of lymphovascular invasion [27]. Thus, a reported incidence of lymphovascuar invasion for colonic carcinomas was quite variable from 0% to 31% using H. and E. sections and immunostained sections for D2-40 and CD 31 [27]- [29].…”

Section: Prox1mentioning

confidence: 82%

“…There was substantial interobserver variability in diagnosing lymphovascular invasion among the six pathologists due to the different individual criteria for lymphovascualr invasion [27]. Immunocytochecmical staining for D2-40 for lymphatic vessels and CD 31 for venous vessels did not improve for identifying lymphovascular invasion using the currently available antibodies [27]. This study highlights the need for criteria in evaluation of lymphovascular invasion [27].…”

Section: Prox1mentioning

confidence: 86%

“…Lymphovascular invasion identified by tumor emboli was 0% to 15% in our cases. Harris et al assembled a group of six GI pathologists to test an interobserver variability by examining 50 cases of moderately-differentiated T 1 to T 3 colorectal carcinoma and the tally was: 7%, 24% and 31% for lymphatic invasion and 0%, 10% and 23% for venous vessels for T 1 , T 2 and T 3 the carcinomas, respectively [27]. There was substantial interobserver variability in diagnosing lymphovascular invasion among the six pathologists due to the different individual criteria for lymphovascualr invasion [27].…”

Section: Prox1mentioning

confidence: 99%

“…Harris et al assembled a group of six GI pathologists to test an interobserver variability by examining 50 cases of moderately-differentiated T 1 to T 3 colorectal carcinoma and the tally was: 7%, 24% and 31% for lymphatic invasion and 0%, 10% and 23% for venous vessels for T 1 , T 2 and T 3 the carcinomas, respectively [27]. There was substantial interobserver variability in diagnosing lymphovascular invasion among the six pathologists due to the different individual criteria for lymphovascualr invasion [27]. Immunocytochecmical staining for D2-40 for lymphatic vessels and CD 31 for venous vessels did not improve for identifying lymphovascular invasion using the currently available antibodies [27].…”

Section: Prox1mentioning

confidence: 99%

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Cancer-Associated Lymphatic and Venous Vessels in Colonic Carcinomas

Tomita¹

2014

OJPathology

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Objective: Colonic carcinomas spread to regional lymph nodes and liver. There are cancer-associated lymphatic and venous vessels at the margin of colonic carcinomas, which facilitate spreading carcinoma through lymphatic and venous vessels. This study aimed to examine cancer-associated lymphatic and venous vessels in TNM T1 to T3 carcinomas using lymphatic vessel hyaluronan receptor for lymphatic vessels and von Willebrand factor for venous vessels by immunocytochemical staining. Materials and Methods: A total of 40 cases of moderately differentiated colonic carcinoma were studied using routinely formalin-fixed and paraffin-embedded sections. The cases consisted of 10 cases of TNM T1, 15 cases each of T2 and T3 cases. Immunocytochemical staining was performed using goat antihuman LYVE-1for lymphatic vessels and rabbit antihuman von Willebrand factor for venous vessels. Results: In TNM T1 carcinoma, increased, irregular and narrow lymphatic and venous vessels were present in the adjacent normal mucosa to the carcinoma, some of which penetrated cancerous lesion. There were no tumor emboli in lymphatic and venous vessels. In TNM T2 carcinoma, there were few lymphatic and venous vessels in midst of the carcinoma whereas numerous small lymphatic and venous vessels were present within muscle layers adjacent to the invading carcinoma. Extramural tumor embolus was present in submucosa in one case. In TNM T3 carcinoma, cancer has invaded through the muscle layers where dilated lymphatic and venous vessels were present adjacent to cancerous nests. Tumor emboli were identified in two cases by immunocytochemical staining. Conclusion: The current study showed cancer-associated lymphatic and venous vessels at the interface in TNM T1 carcinoma to dilated intramuscular lymphatic and venous vessels adjacent to invading cancerous nests in TNM T3 carcinoma, and supports cancerous cells spread via lymphatic and venous vessels through muscle layers to subserosa as supported by tumor emboli in the lymphovascular system. T. Tomita 102

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Pathologist Validation of a Machine Learning–Derived Feature for Colon Cancer Risk Stratification

et al. 2023

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ImportanceIdentifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning–derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists.ObjectiveTo evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer.Design, Setting, and ParticipantsThis prognostic study used deidentified, archived colorectal cancer cases from January 2013 to December 2015 from the University of Milano-Bicocca. All available histologic slides from 258 consecutive colon adenocarcinoma cases were reviewed from December 2021 to February 2022 by 2 pathologists, who conducted semiquantitative scoring for tumor adipose feature (TAF), which was previously identified via a prognostic deep learning model developed with an independent colorectal cancer cohort.Main Outcomes and MeasuresPrognostic value of TAF for overall survival and disease-specific survival as measured by univariable and multivariable regression analyses. Interpathologist agreement in TAF scoring was also evaluated.ResultsA total of 258 colon adenocarcinoma histopathologic cases from 258 patients (138 men [53%]; median age, 67 years [IQR, 65-81 years]) with stage II (n = 119) or stage III (n = 139) cancer were included. Tumor adipose feature was identified in 120 cases (widespread in 63 cases, multifocal in 31, and unifocal in 26). For overall survival analysis after adjustment for tumor stage, TAF was independently prognostic in 2 ways: TAF as a binary feature (presence vs absence: hazard ratio [HR] for presence of TAF, 1.55 [95% CI, 1.07-2.25]; P = .02) and TAF as a semiquantitative categorical feature (HR for widespread TAF, 1.87 [95% CI, 1.23-2.85]; P = .004). Interpathologist agreement for widespread TAF vs lower categories (absent, unifocal, or multifocal) was 90%, corresponding to a κ metric at this threshold of 0.69 (95% CI, 0.58-0.80).Conclusions and RelevanceIn this prognostic study, pathologists were able to learn and reproducibly score for TAF, providing significant risk stratification on this independent data set. Although additional work is warranted to understand the biological significance of this feature and to establish broadly reproducible TAF scoring, this work represents the first validation to date of human expert learning from machine learning in pathology. Specifically, this validation demonstrates that a computationally identified histologic feature can represent a human-identifiable, prognostic feature with the potential for integration into pathology practice.

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Lymphovascular Invasion in Colorectal Cancer

Cited by 169 publications

References 32 publications

CD147 overexpression allows an accurate discrimination of bladder cancer patients’ prognosis

CD147 overexpression allows an accurate discrimination of bladder cancer patients’ prognosis

Cancer-Associated Lymphatic and Venous Vessels in Colonic Carcinomas

Pathologist Validation of a Machine Learning–Derived Feature for Colon Cancer Risk Stratification

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