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Personalized and Integrated Management of Breast Cancer Patient
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Personalized and Integrated Management of Breast Cancer Patient

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 4588

Special Issue Editors

Dr. Gianluca Franceschini

E-Mail Website
Guest Editor
Division of Breast Surgery, Rome Catholic University School of Medicine, Rome, Italy
Interests: breast cancer; breast pathology; breast reconstruction; breast surgery; cellulose; contralateral prophylactic mastectomy; mastectomy; oncoplastic surgery; surgical oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Alba Di Leone

E-Mail Website
Guest Editor
Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Interests: breast cancer; breast pathology; breast reconstruction; breast surgery; cellulose
Special Issues, Collections and Topics in MDPI journals
Dr. Alejandro Martin Sanchez

E-Mail Website
Guest Editor
Alejandro Martin Sanchez, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Breast Cancer Center, Rome, Italy
Interests: breast surgery; surgery; surgical oncology; breast cancer management; breast cancer screening; senology; breast imaging; mammography; breast cancer; breast cancer stem cells
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Breast cancer is considered an international priority in healthcare. Globally, it is the most common malignancy and the leading cause of cancer death in women, with demographic trends indicating a continuous increase in incidence. 

Considerable resources and efforts have been devoted to optimizing systematic strategies in diagnosis and treatment of breast cancer patients over the last few decades; increased population-based screening and improved treatments have contributed to reduce breast cancer mortality rates. 

A multidisciplinary approach involving surgical, radiation, and medical oncology should always be performed in order to optimize oncological and aesthetic outcomes, prolong survival, and improve patient quality of life. Today, the “Breast Unit model” is the gold standard to ensure optimal patient-centered and research-based clinical services by a multidisciplinary and personalized management. 

The selection of the best therapeutic strategy depends on clinical factors, patient status, staging, biologic markers, such as hormone receptor status, human epidermal growth factor receptor 2 (HER2) overexpression, and patient preferences; the decision-making process in breast cancer management should involve a detailed discussion with patients about the benefits but also risks associated with each specific treatment. 

Surgical treatment has gradually evolved toward less aggressive approaches with the adoption of new therapeutic strategies. Evolving evidence-based guidelines in leading disciplines as radiation-medical oncology have led to a significant improvement of survival rates. 

This Special Issue will highlight the innovations in the integrated and personalized management of breast cancer, their potential advantages, and the many open issues that still require proper implementation. A set of evidence-based practice articles of high quality useful in order to optimize the treatment of breast cancer patients by a multidisciplinary approach could be presented thanks to your contribution. 

Dr. Gianluca Franceschini
Dr. Alba Di Leone
Dr. Alejandro Martin Sanchez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • breast cancer
  • breast cancer management
  • breast cancer surgery
  • mastectomy
  • conserving breast surgery
  • oncoplastic surgery
  • radiotherapy
  • medical oncology
  • integrated therapy
  • breast unit
  • biologic markers

Published Papers (2 papers)

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Research

12 pages, 1628 KiB  
Article
Effects of Tumor-Rib Distance and Dose-Dependent Rib Volume on Radiation-Induced Rib Fractures in Patients with Breast Cancer
by Sang Mi Lee, Jeong Won Lee, Woo Chul Kim, Chul Kee Min, Eun Seog Kim and In Young Jo
J. Pers. Med. 2022, 12(2), 240; https://doi.org/10.3390/jpm12020240 - 08 Feb 2022
Cited by 3 | Viewed by 2575
Abstract
This study aimed to investigate the effects of tumor-rib distance and dose-dependent rib volume on radiation-induced rib fractures (RIRFs) in patients with breast cancer. We retrospectively included 510 women with breast cancer who underwent surgical resection with adjuvant radiotherapy. The tumor-rib distance was [...] Read more.
This study aimed to investigate the effects of tumor-rib distance and dose-dependent rib volume on radiation-induced rib fractures (RIRFs) in patients with breast cancer. We retrospectively included 510 women with breast cancer who underwent surgical resection with adjuvant radiotherapy. The tumor-rib distance was measured using preoperative computed tomography (CT) images. Postoperative chest wall thickness and dose-dependent rib volumes, which are absolute rib volumes receiving >20 Gy (V20), 30 Gy (V30), 40 Gy (V40), 45 Gy (V45), and 50 Gy (V50), were measured from the stimulation CT images for radiation treatment planning. We assessed the relationship of RIRF with tumor-rib distance, postoperative chest wall thickness, and dose-dependent rib volumes. Patients with high values of tumor-rib distance and postoperative chest wall thickness had significantly lower risks of RIRF than those with low values. Patients with high values of V20, V30, V40, V45, and V50 had significantly higher risks of RIRF than those with low values. In a multivariate analysis, tumor-rib distance and all five dose-dependent rib volumes, as well as osteoporosis and radiation field, were independent risk factors for RIRF. Tumor-rib distance and dose-dependent rib volume were independent risk factors for RIRF in patients with breast cancer. Full article
(This article belongs to the Special Issue Personalized and Integrated Management of Breast Cancer Patient)
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13 pages, 285 KiB  
Article
Design and Analysis Methods for Trials with AI-Based Diagnostic Devices for Breast Cancer
by Lu Liu, Kevin J. Parker and Sin-Ho Jung
J. Pers. Med. 2021, 11(11), 1150; https://doi.org/10.3390/jpm11111150 - 04 Nov 2021
Viewed by 1528
Abstract
Imaging is important in cancer diagnostics. It takes a long period of medical training and clinical experience for radiologists to be able to accurately interpret diagnostic images. With the advance of big data analysis, machine learning and AI-based devices are currently under development [...] Read more.
Imaging is important in cancer diagnostics. It takes a long period of medical training and clinical experience for radiologists to be able to accurately interpret diagnostic images. With the advance of big data analysis, machine learning and AI-based devices are currently under development and taking a role in imaging diagnostics. If an AI-based imaging device can read the image as accurately as experienced radiologists, it may be able to help radiologists increase the accuracy of their reading and manage their workloads. In this paper, we consider two potential study objectives of a clinical trial to evaluate an AI-based device for breast cancer diagnosis by comparing its concordance with human radiologists. We propose statistical design and analysis methods for each study objective. Extensive numerical studies are conducted to show that the proposed statistical testing methods control the type I error rate accurately and the design methods provide required sample sizes with statistical powers close to pre-specified nominal levels. The proposed methods were successfully used to design and analyze a real device trial. Full article
(This article belongs to the Special Issue Personalized and Integrated Management of Breast Cancer Patient)
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