Advances in analytical approaches for background parenchymal enhancement in predicting breast tumor response to neoadjuvant chemotherapy: A systematic review
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| 出版年: | PLoS One vol. 20, no. 3 (Mar 2025), p. e0317240 |
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Public Library of Science
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| 024 | 7 | |a 10.1371/journal.pone.0317240 |2 doi | |
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| 100 | 1 | |a Thomas, Julius | |
| 245 | 1 | |a Advances in analytical approaches for background parenchymal enhancement in predicting breast tumor response to neoadjuvant chemotherapy: A systematic review | |
| 260 | |b Public Library of Science |c Mar 2025 | ||
| 513 | |a Evidence Based Healthcare Journal Article | ||
| 520 | 3 | |a BackgroundBreast cancer (BC) continues to pose a substantial global health concern, necessitating continuous advancements in therapeutic approaches. Neoadjuvant chemotherapy (NAC) has gained prominence as a key therapeutic strategy, and there is growing interest in the predictive utility of Background Parenchymal Enhancement (BPE) in evaluating the response of breast tumors to NAC. However, the analysis of BPE as a predictive biomarker, along with the techniques used to model BPE changes for accurate and timely predictions of treatment response presents several obstacles. This systematic review aims to thoroughly investigate recent advancements in the analytical methodologies for BPE analysis, and to evaluate their reliability and effectiveness in predicting breast tumor response to NAC, ultimately contributing to the development of personalized and effective therapeutic strategies.MethodsA comprehensive and structured literature search was conducted across key electronic databases, including Cochrane Database of Systematic Reviews, Google Scholar, PubMed, and IEEE Xplore covering articles published up to May 10, 2024. The inclusion criteria targeted studies focusing on breast cancer cohorts treated with NAC, involving both pre-treatment and at least one post-treatment breast dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) scan, and analyzing BPE utility in predicting breast tumor response to NAC. Methodological quality assessment and data extraction were performed to synthesize findings and identify commonalities and differences among various BPE analytical approaches.ResultsThe search yielded a total of 882 records. After meticulous screening, 78 eligible records were identified, with 13 studies ultimately meeting the inclusion criteria for the systematic review. Analysis of the literature revealed a significant evolution in BPE analysis, from early studies focusing on single time-point BPE analysis to more recent studies adopting longitudinal BPE analysis. The review uncovered several gaps that compromise the accuracy and timeliness of existing longitudinal BPE analysis methods, such as missing data across multiple imaging time points, manual segmentation of the whole-breast region of interest, and over reliance on traditional statistical methods like logistic regression for modeling BPE and pathological complete response (pCR).ConclusionThis review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of deep learning-based approaches for automated tumor segmentation, and implementation of advanced AI technique that can better accommodate varied breast tumor responses, non-linear relationships and complex temporal dynamics in BPE datasets, while also handling missing data more effectively. Such integration could lead to more precise and timely predictions of breast tumor responses to NAC, thereby enhancing personalized and effective breast cancer treatment strategies. | |
| 653 | |a Databases | ||
| 653 | |a Tumors | ||
| 653 | |a Mortality | ||
| 653 | |a Segmentation | ||
| 653 | |a Image processing | ||
| 653 | |a Metabolism | ||
| 653 | |a Machine learning | ||
| 653 | |a Evaluation | ||
| 653 | |a Chemotherapy | ||
| 653 | |a Missing data | ||
| 653 | |a Image segmentation | ||
| 653 | |a Quality control | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Effectiveness | ||
| 653 | |a Criteria | ||
| 653 | |a Statistical methods | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Accuracy | ||
| 653 | |a Public health | ||
| 653 | |a Breast cancer | ||
| 653 | |a Cancer therapies | ||
| 653 | |a Medical imaging | ||
| 653 | |a Automation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Deep learning | ||
| 653 | |a Customization | ||
| 653 | |a Magnetic resonance | ||
| 653 | |a Literature reviews | ||
| 653 | |a Patients | ||
| 653 | |a Quality assessment | ||
| 653 | |a Biomarkers | ||
| 653 | |a Global health | ||
| 653 | |a Systematic review | ||
| 653 | |a Social | ||
| 700 | 1 | |a Malla, Lucas | |
| 700 | 1 | |a Benard Shibwabo | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 3 (Mar 2025), p. e0317240 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
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