Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study

Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review

Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer

BACKGROUND

The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics.

METHODS

Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores.

RESULTS

DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001).

CONCLUSIONS

DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Comparison of metastasis and prognosis between early-onset and late-onset hepatocellular carcinoma: A population-based study

Background

While hepatocellular carcinoma (HCC) represents a highly heterogeneous disease with variable oncogenesis mechanisms and biological features, little is understood about differences in distant metastasis (DM) and prognosis between early-onset and late-onset HCC. This study defined early-onset disease as cancer diagnosed at age younger than 50 years and aimed to present a comprehensive analysis to characterize these disparities based on age.

Methods

Information of HCC patients was retrospectively collected from the SEER database and our hospital. Patient demographics, tumor characteristics, and survival were compared between the two groups. A 1:1 propensity score matching (PSM) was adopted to adjust confounding factors. Logistic and cox analysis were utilized to explore risk factors of DM and prognosis, respectively. Besides, the survival differences were assessed by the Kaplan-Meier curve and log-rank test.

Results

In total, 19187 HCC patients obtained from the SEER database and 129 HCC patients obtained from our own center were enrolled. Among 19187 patients with HCC, 3376 were identified in the matched cohort, including 1688 early-onset patients and 1688 late-onset patients. Compared with late-onset HCC, early-onset HCC was more likely to occur in female (25.2% vs. 22.9%, P = 0.030), have large tumors (>10.0 cm, 24.1% vs. 14.6%, P = 0.000), harbor poorly differentiated/undifferentiated cancers (17.0% vs. 14.0%, P = 0.003), present advanced clinical stage (T3+T4, 33.7% vs. 28.5%; N1, 9.2% vs. 6.7%; P = 0.000), and develop DM (13.0% vs. 9.5%, P = 0.000). After adjustment for confounders by PSM, we discovered that early-onset HCC remained an independent risk factor for DM. However, combined with Kaplan-Meier curve and cox analysis, early-onset HCC was an independent favorable predictor of survival. We validated these data on an independent cohort from our hospital.

Conclusion

In this population-based study, despite developing DM more frequently, early-onset HCC exhibited a superior prognosis than late-onset HCC. Nevertheless, further research is warranted to understand the underlying aetiologic basis for the disparities.

Short-term postoperative outcomes of lymphadenectomy for cholangiocarcinoma, hepatocellular carcinoma and colorectal liver metastases in the modern era of liver surgery: Insights from the StuDoQ|Liver registry

INTRODUCTION

The clinical role of lymphadenectomy (LAD) as part of hepatic resection for malignancies of the liver remains unclear. In this study, we aimed to report on the use cases and postoperative outcomes of liver resection and simultaneous LAD for hepatic malignancies (HM).

MATERIALS AND METHODS

Clinicopathological data from patients who underwent surgery at 13 German centers from 2017 to 2022 (n = 3456) was extracted from the StuDoQ|Liver registry of the German Society of General and Visceral Surgery. Propensity-score matching (PSM) was performed to account for the extent of liver resection and patient demographics.

RESULTS

LAD was performed in 545 (16%) cases. The most common indication for LAD was cholangiocarcinoma (CCA), followed by colorectal liver metastases (CRLM) and hepatocellular carcinoma (HCC). N+ status was found in 7 (8%), 59 (35%), and 56 cases (35%) for HCC, CCA, and CRLM, respectively (p < 0.001). The LAD rate was highest for robotic-assisted resections (28%) followed by open (26%) and laparoscopic resections (13%), whereas the number of resected lymph nodes was equivalent between the techniques (p = 0.303). LAD was associated with an increased risk of liver-specific postoperative complications, especially for patients with HCC.

CONCLUSION

In this multicenter registry study, LAD was found to be associated with an increased risk of liver-specific complications. The highest rate of LAD was observed among robotic liver resections.

Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study

OBJECTIVES

To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer.

METHODS

Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology.

RESULTS

One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64).

CONCLUSIONS

Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging.

CLINICAL RELEVANCE STATEMENT

The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT.

KEY POINTS

• Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts. • The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves. • Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment

Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.