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Medicina
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Future Trends in Breast Cancer Management
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Future Trends in Breast Cancer Management

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 881

Special Issue Editor

Dr. Dinesh K. Thekkinkattil

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Guest Editor
Department of General Surgery, National Health Service | NHS, Lincoln, UK
Interests: breast cancer; breast reconstruction; lipomodelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer is one of the most common adult cancers worldwide with more than 2.3 million cases of breast cancer occurring each year. The management of breast cancer is a quickly changing field. The adoption of newer technologies and quality research has helped better diagnosis, prognostication, and treatments. In addition, there have been advancements in the availability of wider targeted therapies to treat breast cancer with improved outcomes. Ongoing research in various fields and a better understanding of this heterogeneous disease is helping to adopt various de-escalation treatment settings. Further research into various subsets of breast cancer patients would be of interest, such as Her2-low breast cancer patients and treatment outcomes with various antibody drug conjugates. Artificial intelligence and digital technology will significantly influence the management of breast cancer in the future. This may be of great help to achieve a consistent improved cancer survival with better patient outcomes being reported across the globe. Advancements in radiomics may enable us to decipher the histological nature of the disease without invasive tissue biopsies.

Dr. Dinesh K. Thekkinkattil
Guest Editor

Manuscript Submission Information

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Keywords

  • breast cancer
  • breast reconstruction
  • artificial intelligence
  • machine learning
  • staging
  • treatment
  • targeted therapy
  • digital pathology
  • radiomics
  • oncoplastic surgery
  • prognosis
  • immunotherapy
  • liquid biopsy

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Published Papers (1 paper)

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Research

15 pages, 972 KiB  
Article
An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer—A Multicentric Study
by Simona Parisi, Francesco Saverio Lucido, Federico Maria Mongardini, Roberto Ruggiero, Francesca Fisone, Salvatore Tolone, Antonio Santoriello, Francesco Iovino, Domenico Parmeggiani, David Vagni, Loredana Cerbara, Ludovico Docimo and Claudio Gambardella
Medicina 2024, 60(11), 1806; https://doi.org/10.3390/medicina60111806 - 4 Nov 2024
Viewed by 643
Abstract
Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US [...] Read more.
Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p < 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p < 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p < 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES > 137 kPa and RI < 1.55), and conversely, a negative result was predicted if both were absent (SWES < 137 kPa and RI > 1.55). In the second tier, if there was a mix of the results (SWES > 137 kPa and RI > 1.55 or SWES < 137 kPa and RI < 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers. Full article
(This article belongs to the Special Issue Future Trends in Breast Cancer Management)
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