A fully automatic framework for evaluating cosmetic results of breast conserving therapy
The breast cosmetic outcome after breast conserving therapy is essential for evaluating breast treatment and determining patient’s remedy selection. This prompts the need of objective and efficient methods for breast cosmesis evaluations. However, current evaluation methods rely on ratings from a sm...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022001050 |
| _version_ | 1811177415828832256 |
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| author | Chenqi Guo Tamara L. Smith Qianli Feng Fabian Benitez-Quiroz Frank Vicini Douglas Arthur Julia White Aleix Martinez |
| author_facet | Chenqi Guo Tamara L. Smith Qianli Feng Fabian Benitez-Quiroz Frank Vicini Douglas Arthur Julia White Aleix Martinez |
| author_sort | Chenqi Guo |
| collection | DOAJ |
| description | The breast cosmetic outcome after breast conserving therapy is essential for evaluating breast treatment and determining patient’s remedy selection. This prompts the need of objective and efficient methods for breast cosmesis evaluations. However, current evaluation methods rely on ratings from a small group of physicians or semi-automated pipelines, making the processes time-consuming and their results inconsistent. To solve the problem, in this study, we proposed: 1. a fully-automatic Machine Learning Breast Cosmetic evaluation algorithm leveraging the state-of-the-art Deep Learning algorithms for breast detection and contour annotation, 2. a novel set of Breast Cosmesis features, 3. a new Breast Cosmetic dataset consisting 3k+ images from three clinical trials with human annotations on both breast components and their cosmesis scores. We show our fully-automatic framework can achieve comparable performance to state-of-the-art without the need of human inputs, leading to a more objective, low-cost and scalable solution for breast cosmetic evaluation in breast cancer treatment. |
| first_indexed | 2024-04-11T06:01:28Z |
| format | Article |
| id | doaj.art-f8743db0601a472c91db49631b2641ae |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-04-11T06:01:28Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-f8743db0601a472c91db49631b2641ae2022-12-22T04:41:39ZengElsevierMachine Learning with Applications2666-82702022-12-0110100430A fully automatic framework for evaluating cosmetic results of breast conserving therapyChenqi Guo0Tamara L. Smith1Qianli Feng2Fabian Benitez-Quiroz3Frank Vicini4Douglas Arthur5Julia White6Aleix Martinez7Computational Biology and Cognitive Science Laboratory, the Ohio State University, Columbus, OH, USA; Correspondence to: Computational Biology and Cognitive Science Laboratory, the Ohio State University, Columbus, OH 43210, USA.Radiation Oncology, Memorial Healthcare System, Hollywood, FL, USAComputational Biology and Cognitive Science Laboratory, the Ohio State University, Columbus, OH, USAComputational Biology and Cognitive Science Laboratory, the Ohio State University, Columbus, OH, USARadiation Oncology, Genesis Care Pty Ltd, Alexandria, NSW, AustraliaRadiation Oncology, Virginia Commonwealth University, Richmond, VA, USARadiation Oncology, the Ohio State University, Columbus, OH, USAComputational Biology and Cognitive Science Laboratory, the Ohio State University, Columbus, OH, USAThe breast cosmetic outcome after breast conserving therapy is essential for evaluating breast treatment and determining patient’s remedy selection. This prompts the need of objective and efficient methods for breast cosmesis evaluations. However, current evaluation methods rely on ratings from a small group of physicians or semi-automated pipelines, making the processes time-consuming and their results inconsistent. To solve the problem, in this study, we proposed: 1. a fully-automatic Machine Learning Breast Cosmetic evaluation algorithm leveraging the state-of-the-art Deep Learning algorithms for breast detection and contour annotation, 2. a novel set of Breast Cosmesis features, 3. a new Breast Cosmetic dataset consisting 3k+ images from three clinical trials with human annotations on both breast components and their cosmesis scores. We show our fully-automatic framework can achieve comparable performance to state-of-the-art without the need of human inputs, leading to a more objective, low-cost and scalable solution for breast cosmetic evaluation in breast cancer treatment.http://www.sciencedirect.com/science/article/pii/S2666827022001050Breast cancerBreast conserving therapyBreast Cosmesis scoresBreast detectionMachine learningPredictive model |
| spellingShingle | Chenqi Guo Tamara L. Smith Qianli Feng Fabian Benitez-Quiroz Frank Vicini Douglas Arthur Julia White Aleix Martinez A fully automatic framework for evaluating cosmetic results of breast conserving therapy Machine Learning with Applications Breast cancer Breast conserving therapy Breast Cosmesis scores Breast detection Machine learning Predictive model |
| title | A fully automatic framework for evaluating cosmetic results of breast conserving therapy |
| title_full | A fully automatic framework for evaluating cosmetic results of breast conserving therapy |
| title_fullStr | A fully automatic framework for evaluating cosmetic results of breast conserving therapy |
| title_full_unstemmed | A fully automatic framework for evaluating cosmetic results of breast conserving therapy |
| title_short | A fully automatic framework for evaluating cosmetic results of breast conserving therapy |
| title_sort | fully automatic framework for evaluating cosmetic results of breast conserving therapy |
| topic | Breast cancer Breast conserving therapy Breast Cosmesis scores Breast detection Machine learning Predictive model |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022001050 |
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