Prognostic Role of Lymphovascular Invasion in Patients with Early Breast Cancer

Automated breast volume scanner based Radiomics for non-invasively prediction of lymphovascular invasion status in breast cancer

PURPOSE

Lymphovascular invasion (LVI) indicates resistance to preoperative adjuvant chemotherapy and a poor prognosis and can only be diagnosed by postoperative pathological examinations in breast cancer. Thus, a technique for preoperative diagnosis of LVI is urgently needed. We aim to explore the ability of an automated breast volume scanner (ABVS)-based radiomics model to noninvasively predict the LVI status in breast cancer.

METHODS

We conducted a retrospective analysis of data from 335 patients diagnosed with T1-3 breast cancer between October 2019 and September 2022. The patients were divided into training cohort and validation cohort with a ratio of 7:3. For each patient, 5901 radiomics features were extracted from ABVS images. Feature selection was performed using LASSO method. We created machine learning models for different feature sets with support vector machine algorithm to predict LVI. And significant clinicopathologic factors were identified by univariate and multivariate logistic regression to combine with three radiomics signatures as to develop a fusion model.

RESULTS

The three SVM-based prediction models, demonstrated relatively high efficacy in identifying LVI of breast cancer, with AUCs of 79.00%, 80.00% and 79.40% and an accuracy of 71.00%, 80.00% and 75.00% in the validation cohort for AP, SP and CP plane image. The fusion model achieved the highest AUC of 87.90% and an accuracy of 85.00% in the validation cohort.

CONCLUSIONS

The combination of radiomics features from ABVS images and an SVM prediction model showed promising performance for preoperative noninvasive prediction of LVI in breast cancer.

Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer

Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.

An evaluation of lymphovascular invasion in relation to biology and prognosis according to subtypes in invasive breast cancer

Lymphovascular invasion (LVI) is associated with a poor outcome in breast cancer. The purpose of the present study was to evaluate the clinical significance of LVI in primary breast cancer and to investigate disease-free survival as a prognostic marker according to the breast cancer subtypes. This study examined 4,652 consecutive cases of invasive breast cancer excluding the patients with non-invasive cancer, stage IV and those who underwent neo-adjuvant therapy from February 2002 to February 2021. The clinicopathological characteristics and prognosis of LVI-positive and -negative tumors were compared. LVI was evaluated in H&E staining specimens from surgically resected samples. The LVI expression rates were 29.2% (low, 19.7%; high, 9.5%) in all primary cases. The LVI-positive rate was significantly associated with specimens with the following characteristics: ER/PgR-negative, HER2-positive, p53 overexpression, higher Ki-67 index values, higher nuclear grade, positive nodes and larger tumors. Moreover, the subtypes were significantly associated with LVI positivity; 20% in Luminal A, 34.6% in Luminal B, 40.9% in Lumina/HER2, 38.1% in HER2-enriched and 29.8% in triple negative (TN). There were significant differences in disease-free survival between LVI status in Luminal A, Luminal B and TN subtypes, but there was no difference in the Luminal/HER2 and HER2-enriched subtypes. A multivariate analysis revealed that LVI was a significant factor in Luminal B and TN subtypes. Overall, LVI was significantly associated with the advanced and aggressive characteristics in breast cancer. Luminal A type had a lower LVI rate, and HER2 type had a higher LVI rate. Moreover, LVI was a significant prognostic factor in Luminal B and TN subtypes. These data suggested that the LVI status was useful in predicting the prognosis in HER2 negative breast cancer cases.