Clinical Studies on Breast Lymph Node Involvement

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (5 May 2022) | Viewed by 3624

Special Issue Editors

I.R.C.C.S. 'Giovanni Paolo II' Istituto Tumori, 70124 Bari, Italy
Interests: radiomics analysis; medical physics; mammography; breasts; lobular carcinoma
I.R.C.C.S. 'Giovanni Paolo II' Istituto Tumori, 70124 Bari, Italy
Interests: breast cancer; artificiale intelligence in medicine; automated computer-aided system
I.R.C.C.S. 'Giovanni Paolo II' Istituto Tumori, 70124 Bari, Italy
Interests: radiomics analysis; medical physics; mammography; breasts, lobular carcinoma

Special Issue Information

Dear Colleagues,

Breast cancer surgery has evolved over the years. In the past, all patients with invasive breast cancer were subjected a complete axillary lymph node dissection and were therefore at risk of suffering from its associated high morbidity. The current guidelines provide the removal of sentinel lymph node biopsy (SLNB), which are the first axillary draining lymph nodes, for all clinically negative patients at the clinical or radiological exam. SLNB has become the standard of care and represents a significant advance toward reducing invasive procedures for armpit management. However, SBLN is also an invasive procedure that is not without complications.

The prediction of lymph node involvement in breast cancer represents an important task, which could reduce unnecessary surgery, improve the definition of personalized oncological therapies, and predict the lymph node involvement that affects the risk of recurrence and survival. Continuous improvement of the tumor molecular profile, radiomic and genetic signature, and bioinformatics on larger and better identified patient cohorts allow us to answer important questions related to the axillary metastatic process.

The Special Issue aims to provide an overview of the current knowledge in this field by gathering studies on potential biomarkers (e.g., diagnostic, response, predictive, prognostic, susceptibility, therapeutic) and predictive models of breast lymph node involvement.

Dr. Raffaella Massafra
Dr. Annarita Fanizzi
Dr. Vito Lorusso
Guest Editors

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Keywords

  • breast cancer
  • lympn node axillaty
  • sentinel lympn node
  • sentinel lymph node biopsy
  • survivall
  • recurrence
  • relapse, machine learning
  • deep learning
  • artificial intelligence
  • prediction model
  • diagnostic and prognostic support tools
  • biomarkers

Published Papers (2 papers)

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Research

13 pages, 579 KiB  
Article
Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
by Annarita Fanizzi, Vito Lorusso, Albino Biafora, Samantha Bove, Maria Colomba Comes, Cristian Cristofaro, Maria Digennaro, Vittorio Didonna, Daniele La Forgia, Annalisa Nardone, Domenico Pomarico, Pasquale Tamborra, Alfredo Zito, Angelo Virgilio Paradiso and Raffaella Massafra
Appl. Sci. 2021, 11(21), 10372; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110372 - 04 Nov 2021
Cited by 7 | Viewed by 1578
Abstract
The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with [...] Read more.
The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature. Full article
(This article belongs to the Special Issue Clinical Studies on Breast Lymph Node Involvement)
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10 pages, 744 KiB  
Article
Disease-Free Survival after Breast Conservation Therapy vs. Mastectomy of Patients with T1/2 Breast Cancer and No Lymph Node Metastases: Our Experience
by Annarita Fanizzi, Maurizio Cosmo Ressa, Gianluca Gatta, Cristian Cristofaro, Valerio De Santis, Vittorio Didonna, Sergio Diotaiuti, Daniele La Forgia, Nicole Petruzzellis, Pasquale Tamborra, Vito Lorusso and Raffaella Massafra
Appl. Sci. 2021, 11(21), 9800; https://0-doi-org.brum.beds.ac.uk/10.3390/app11219800 - 20 Oct 2021
Cited by 2 | Viewed by 1342
Abstract
Several retrospective analyses of large amounts of contemporary data have shown the superiority of breast conservative surgery (BCS) over mastectomy carried out in the early stage of breast cancer. The characteristics of the patients and cancers that are most likely to benefit from [...] Read more.
Several retrospective analyses of large amounts of contemporary data have shown the superiority of breast conservative surgery (BCS) over mastectomy carried out in the early stage of breast cancer. The characteristics of the patients and cancers that are most likely to benefit from BCS remain unclear. In our work, we analyzed the disease-free survival (DFS) of a cohort of patients treated with BCS or mastectomy between 1995 and 2018 in our institute with pT1-2, pN0, or cM0 breast cancer. The DFS curves of patients treated with both mastectomy and quadrantectomy were compared in the different subsamples with respect to the clinical and histopathological characteristics. We identified 188 eligible patients treated with BCS and 64 patients treated with mastectomy. DFS was not found to be statistically higher in patients treated with BCS compared to those treated with mastectomy, who achieved a 5-year DFS of 89.9% vs. 81.3% and a 10-year DFS of 78.9% vs. 79.3%, respectively. No significant differences were detected for the DFS curves when patients were differentiated by the type of surgical treatment received, age, and the tumor histological characteristics. We verified a p-value just above the 10% significance threshold for patients with tumor dimensions between 20 mm and 50 mm and molecular sub-type Luminal B. In our experience, treatment with mastectomy is not associated with improved DFS compared to treatment with BCS in women with early-stage tumors. Full article
(This article belongs to the Special Issue Clinical Studies on Breast Lymph Node Involvement)
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