The Usefulness of Bayesian Network in Assessing the Risk of Triple-Negative Breast Cancer

Bayesian Networks in Radiology

A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values. Bayesian networks can learn their structure (nodes and connections) and/or conditional probability values from data. Bayesian networks offer several advantages: (a) they can efficiently perform complex inferences, (b) reason from cause to effect or vice versa, (c) assess counterfactual data, (d) integrate observations with canonical ("textbook") knowledge, and (e) explain their reasoning. Bayesian networks have been employed in a wide variety of applications in radiology, including diagnosis and treatment planning. Unlike deep learning approaches, Bayesian networks have not been applied to computer vision. However, hybrid artificial intelligence systems have combined deep learning models with Bayesian networks, where the deep learning model identifies findings in medical images and the Bayesian network formulates and explains a diagnosis from those findings. One can apply a Bayesian network's probabilistic knowledge to integrate clinical and imaging findings to support diagnosis, treatment planning, and clinical decision-making. This article reviews the fundamental principles of Bayesian networks and summarizes their applications in radiology. Keywords: Bayesian Network, Machine Learning, Abdominal Imaging, Musculoskeletal Imaging, Breast Imaging, Neurologic Imaging, Radiology Education Supplemental material is available for this article. © RSNA, 2023.

Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI

RATIONALE AND OBJECTIVES

To investigate whether machine learning (ML) approaches using breast magnetic resonance imaging (MRI)-derived multiparametric and radiomic features could predict axillary lymph node metastasis (ALNM) in stage I-II triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

Between 2013 and 2019, 86 consecutive patients with TNBC who underwent preoperative MRI and surgery were enrolled and divided into ALNM (N = 27) and non-ALNM (n = 59) groups according to histopathologic results. For multiparametric features, kinetic features using computer-aided diagnosis (CAD), morphologic features, and apparent diffusion coefficient (ADC) values at diffusion-weighted images were evaluated. For extracting radiomic features, three-dimensional segmentation of tumors using T2-weighted images (T2WI) and T1-weighted subtraction images were respectively performed by two radiologists. Each predictive model using three ML algorithms was built using multiparametric features or radiomic features, or both. The diagnostic performances of models were compared using the DeLong method.

RESULTS

Among multiparametric features, non-circumscribed margin, peritumoral edema, larger tumor size, and larger angio-volume at CAD were associated with ALNM in univariate analysis. In multivariate analysis, larger angio-volume was the sole statistically significant predictor for ALNM (odds ratio = 1.33, P = 0.008). Regarding ADC values, there were no significant differences according to ALNM status. The area under the receiver operating characteristic curve for predicting ALNM was 0.74 using multiparametric features, 0.77 using radiomic features from T1-weighted subtraction images, 0.80 using radiomic features from T2WI, and 0.82 using all features.

CONCLUSION

A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.

Computational Tactics for Precision Cancer Network Biology

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

Silencing of Pyruvate Kinase M2 via a Metal-Organic Framework Based Theranostic Gene Nanomedicine for Triple-Negative Breast Cancer Therapy

Triple-negative breast cancer (TNBC) is typically associated with poor prognosis due to its only partial response to chemotherapy and lack of clinically established targeted therapies coupled with an aggressive disease course. Aerobic glycolysis is a hallmark of reprogrammed metabolic activity in cancer cells, which can be repressed by small-interfering RNA (siRNA). However, the lack of effective carriers to deliver vulnerable siRNA restricts the clinical potentials of glycolysis-based gene therapy for TNBC. Herein, we develop a tumor-targeted, biomimetic manganese dioxide (MnO₂)-shrouded metal-organic framework (MOF) based nanomedicine to deliver siRNA against pyruvate kinase muscle isozyme M2 (siPKM2), wherein PKM2 is a rate-limiting enzyme in glycolysis, to inhibit the reprogrammed glycolysis of TNBC. This MOF-based genetic nanomedicine shows excellent monodispersity and stability and protects siPKM2 against degradation by nucleases. The nanomedicine not only substantially blocks the glycolytic pathway but also improves intracellular hypoxia in TNBC cells, with a resultant O₂-enhanced anticancer effect. In the mice orthotopic TNBC model, the nanomedicine shows a remarkable therapeutic effect. Meanwhile, the Mn²⁺ ions released from acid microenvironment-responsive MnO₂ enable in vivo monitoring of the therapeutic process with magnetic resonance imaging (MRI). Our study shows great promise with this MRI-visible MOF-based nanomedicine for treating TNBC by inhibition of glycolysis via the RNA interference.