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Current Oncology
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Personalized Care for Breast Cancer: Where Modern Technologies Meet an...
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Personalized Care for Breast Cancer: Where Modern Technologies Meet an Integrated Approach to Diagnosis and Treatment Planning

A special issue of Current Oncology (ISSN 1718-7729). This special issue belongs to the section "Palliative and Supportive Care".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 8785

Special Issue Editor

Prof. Dr. Claire M.B. Holloway

E-Mail Website
Guest Editor
Department of Surgery, University of Toronto, Toronto, ON, Canada
Interests: Cancer diagnosis; breast assessment; clinical pathway maps; breast cancer; pathway concordance; health policy

Special Issue Information

Dear Colleagues,

Scientific advances have laid the foundation for the modern era of breast screening, early diagnosis, and personalized treatment. Advances in breast imaging now permit the early detection and accurate estimation of disease extent in order to facilitate treatment planning. The identification of smaller lesions has necessitated the development of image-guided biopsy techniques adapted for all breast imaging modalities. These techniques have improved the accuracy of breast diagnosis such that they are now the standard for the diagnosis of breast lesions. The determination of tumor invasion and biomarkers on biopsy samples permit enhanced treatment planning at the outset, allowing for definitive surgery at the first surgical procedure and optimal sequencing of multimodality treatment. Furthermore, accurate and informative diagnostic information facilitates the coordination of additional services to address individual needs, including genetic counseling, fertility preservation, psychosocial support, and breast reconstruction as required. For the benefits of personalized care to be realized by all women with breast cancer, a coordinated system approach to provide access to all services is required. This Special Issue will explore advances in technologies and system design during diagnostic assessment and treatment planning aimed at bringing high-quality individualized care to all affected women.

Prof. Dr. Claire M.B. Holloway
Guest Editor

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. Current Oncology 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 2200 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
  • diagnosis
  • image-guided biopsy
  • clinical pathways
  • surgery
  • organized breast assessment
  • high-risk breast lesions
  • treatment planning
  • biomarkers

Published Papers (3 papers)

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Research

30 pages, 1197 KiB  
Article
Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation
by Claire M. B. Holloway, Omid Shabestari, Maria Eberg, Katharina Forster, Paula Murray, Bo Green, Ali Vahit Esensoy, Andrea Eisen and Jonathan Sussman
Curr. Oncol. 2022, 29(8), 5338-5367; https://0-doi-org.brum.beds.ac.uk/10.3390/curroncol29080424 - 28 Jul 2022
Cited by 3 | Viewed by 2135
Abstract
Breast cancer recurrence is an important outcome for patients and healthcare systems, but it is not routinely reported in cancer registries. We developed an algorithm to identify patients who experienced recurrence or a second case of primary breast cancer (combined as a “second [...] Read more.
Breast cancer recurrence is an important outcome for patients and healthcare systems, but it is not routinely reported in cancer registries. We developed an algorithm to identify patients who experienced recurrence or a second case of primary breast cancer (combined as a “second breast cancer event”) using administrative data from the population of Ontario, Canada. A retrospective cohort study design was used including patients diagnosed with stage 0-III breast cancer in the Ontario Cancer Registry between 1 January 2009 and 31 December 2012 and alive six months post-diagnosis. We applied the algorithm to healthcare utilization data from six months post-diagnosis until death or 31 December 2013, whichever came first. We validated the algorithm’s diagnostic accuracy against a manual patient record review (n = 2245 patients). The algorithm had a sensitivity of 85%, a specificity of 94%, a positive predictive value of 67%, a negative predictive value of 98%, an accuracy of 93%, a kappa value of 71%, and a prevalence-adjusted bias-adjusted kappa value of 85%. The second breast cancer event rate was 16.5% according to the algorithm and 13.0% according to manual review. Our algorithm’s performance was comparable to previously published algorithms and is sufficient for healthcare system monitoring. Administrative data from a population can, therefore, be interpreted using new methods to identify new outcome measures. Full article
(This article belongs to the Special Issue Personalized Care for Breast Cancer: Where Modern Technologies Meet an Integrated Approach to Diagnosis and Treatment Planning)
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14 pages, 1240 KiB  
Article
PIK3CA Mutation as Potential Poor Prognostic Marker in Asian Female Breast Cancer Patients Who Received Adjuvant Chemotherapy
by Yoon Ah Cho, Seung Yeon Ko, Yong Joon Suh, Sanghwa Kim, Jung Ho Park, Hye-Rim Park, Jinwon Seo, Hyo Geun Choi, Ho Suk Kang, Hyun Lim, Ha Young Park and Mi Jung Kwon
Curr. Oncol. 2022, 29(5), 2895-2908; https://0-doi-org.brum.beds.ac.uk/10.3390/curroncol29050236 - 19 Apr 2022
Cited by 8 | Viewed by 2793
Abstract
Background: The prognostic relevance of the PIK3CA mutation together with PD-L1, c-Met, and mismatch repair deficiency (dMMR) have not been fully investigated in Asian women with breast cancer (BC) who have undergone postoperative adjuvant chemotherapy. Methods: We analyzed PIK3CA mutations via peptide nucleic [...] Read more.
Background: The prognostic relevance of the PIK3CA mutation together with PD-L1, c-Met, and mismatch repair deficiency (dMMR) have not been fully investigated in Asian women with breast cancer (BC) who have undergone postoperative adjuvant chemotherapy. Methods: We analyzed PIK3CA mutations via peptide nucleic acid (PNA)-mediated real-time PCR assay, PD-L1/c-Met expression via immunohistochemistry (IHC), and microsatellite instability (MSI) status using PCR and IHC, in 191 resected BCs from 2008 to 2011. The Cancer Genome Atlas (TCGA) dataset for the involvement of the PIK3CA mutation with PD-L1/c-Met/MMR was explored. Results: The PNA clamp-mediated assay was able to detect the PIK3CA mutation in 1% of the mutant population in the cell line validation. Using this method, the PIK3CA mutation was found in 78 (49.4%) of 158 samples. c-Met and PD-L1 positivity were identified in 31.4 and 21.8% of samples, respectively, which commonly correlated with high histologic grade and triple-negative subtype. MSI/dMMR was observed in 8.4% of patients, with inconsistency between MMR IHC and the MSI PCR. The PIK3CA mutation exhibited a poor prognostic association regarding recurrence-free survival (RFS) in both overall and triple-negative BCs. In subgroup analyses, the PIK3CA-mutated tumors showed poorer RFS than the PIK3CA-wildtype within the c-Met-positive, MSS, triple-negative, or age onset <50 years subgroups, which showed a similar trend of association in TCGA data. Conclusions: PIK3CA mutation together with c-Met or dMMR/MSI status might be relevant to poor prognosis in BC subsets, especially in Asian women. Full article
(This article belongs to the Special Issue Personalized Care for Breast Cancer: Where Modern Technologies Meet an Integrated Approach to Diagnosis and Treatment Planning)
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15 pages, 2512 KiB  
Article
The OncoSim-Breast Cancer Microsimulation Model
by Jean H. E. Yong, Claude Nadeau, William M. Flanagan, Andrew J. Coldman, Keiko Asakawa, Rochelle Garner, Natalie Fitzgerald, Martin J. Yaffe and Anthony B. Miller
Curr. Oncol. 2022, 29(3), 1619-1633; https://0-doi-org.brum.beds.ac.uk/10.3390/curroncol29030136 - 03 Mar 2022
Cited by 5 | Viewed by 3070
Abstract
Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive [...] Read more.
Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive and ductal carcinoma in situ tumours. It combines Canadian cancer incidence, mortality, screening program, and cost data to project population-level outcomes. Users can change the model input to answer specific questions. Here, we compared its projections with observed data. First, we compared the model’s projected breast cancer trends with the observed data in the Canadian Cancer Registry and from Vital Statistics. Next, we replicated a screening trial to compare the model’s projections with the trial’s observed screening effects. Results: OncoSim-Breast’s projected incidence, mortality, and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and from Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Conclusions: OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer. Full article
(This article belongs to the Special Issue Personalized Care for Breast Cancer: Where Modern Technologies Meet an Integrated Approach to Diagnosis and Treatment Planning)
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