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Diagnostics
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Challenges and Advances in Radiomics and Artificial Intelligence for Breast...
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Challenges and Advances in Radiomics and Artificial Intelligence for Breast Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1928

Special Issue Editor

Dr. Sung-eun Song

E-Mail Website
Guest Editor
College of Medicine, Korea University Anam Hospital, Seoul, Republic of Korea
Interests: breast MRI; breast US; mammography; CAD; AI

Special Issue Information

Dear Colleagues,

Radiomics and artificial intelligence (AI) are emerging technologies with the potential to revolutionize the diagnosis and treatment of breast cancer. Radiomics involves the extraction of quantitative features from medical images to provide valuable information about tumor characteristics and behavior, while AI algorithms can analyze these data to generate predictive models that aid in making informed decisions about patient care. The Special Issue entitled “Challenges and Advances in Radiomics and Artificial Intelligence for Breast Cancer” will cover research articles, case presentations, and literature reviews on the challenges and advancements in using these technologies for the diagnosis and treatment of breast cancer.

This Special Issue will explore, but is not restricted to, the following topics:

  • Radiomics and AI for breast cancer diagnosis and staging;
  • Radiomics and AI for the differentiation of benign and malignant breast lesions;
  • Radiomics and AI for the prediction of treatment response in breast cancer patients;
  • Radiomics and AI for the preidction of breast cancer recurrence;
  • Advancements in imaging technology and methods for breast cancer diagnosis and treatment;
  • Challenges and limitations of using radiomics and AI for breast cancer diagnosis and treatment.

By addressing these topics, the Special Issue aims to contribute to the scientific discussion and understanding of the challenges and advancements in radiomics and artificial intelligence for breast cancer.

Dr. Sung-eun Song
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. Diagnostics is an international peer-reviewed open access semimonthly 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

  • radiomics
  • artificial intelligence
  • deep learning
  • machine learning
  • breast cancer
  • diagnosis
  • segmentation
  • medical imaging
  • risk prediction
  • treatment

Published Papers (2 papers)

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Research

15 pages, 3837 KiB  
Article
Tumor Heterogeneity of Breast Cancer Assessed with Computed Tomography Texture Analysis: Association with Disease-Free Survival and Clinicopathological Prognostic Factor
by Hyeongyu Yoo, Kyu Ran Cho, Sung Eun Song, Yongwon Cho, Seung Pil Jung and Kihoon Sung
Diagnostics 2023, 13(23), 3569; https://doi.org/10.3390/diagnostics13233569 - 29 Nov 2023
Viewed by 667
Abstract
Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and prognostic factors in patients with invasive breast cancer. [...] Read more.
Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and prognostic factors in patients with invasive breast cancer. A total of 256 consecutive women who underwent preoperative chest CT and surgery in our institution were included. The Cox proportional hazards model was used to determine the relationship between textural features and DFS. Logistic regression analysis was used to reveal the relationship between textural features and prognostic factors. Of 256 patients, 21 (8.2%) had disease recurrence over a median follow-up of 60 months. For the prediction of shorter DFS, higher histological grade (hazard ratio [HR], 6.12; p < 0.001) and lymphovascular invasion (HR, 2.93; p = 0.029) showed significance, as well as textural features such as lower mean attenuation (HR, 4.71; p = 0.003) and higher entropy (HR, 2.77; p = 0.036). Lower mean attenuation showed a correlation with higher tumor size, and higher entropy showed correlations with higher tumor size and Ki-67. In conclusion, CTTA-derived textural features can be used as a noninvasive imaging biomarker to predict shorter DFS and prognostic factors in patients with invasive breast cancer. Full article
(This article belongs to the Special Issue Challenges and Advances in Radiomics and Artificial Intelligence for Breast Cancer)
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13 pages, 3241 KiB  
Article
Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features
by Jiwoo Park, Min Jung Kim, Jong-Hyun Yoon, Kyunghwa Han, Eun-Kyung Kim, Joo Hyuk Sohn, Young Han Lee and Yangmo Yoo
Diagnostics 2023, 13(19), 3031; https://doi.org/10.3390/diagnostics13193031 - 23 Sep 2023
Viewed by 923
Abstract
Background: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. Methods: We included 265 ER+HER2- LABC patients (2010–2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using [...] Read more.
Background: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. Methods: We included 265 ER+HER2- LABC patients (2010–2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. Results: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). Conclusions: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC. Full article
(This article belongs to the Special Issue Challenges and Advances in Radiomics and Artificial Intelligence for Breast Cancer)
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