Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images

Liu, Gengbo; Mitra, Debasis; Jones, Ella F.; Franc, Benjamin L.; Behr, Spencer C.; Nguyen, Alex; Bolouri, Marjan S.; Wisner, Dorota J.; Joe, Bonnie N.; Esserman, Laura J.; Hylton, Nola M.; Seo, Youngho

doi:10.1007/s10278-021-00449-y

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Here, AI models also show a superior specificity and a comparable sensitivity when compared to the best standard (radiologists) ( 95 - 98 ). Models have also been designed and successfully applied to predict the molecular subtype of breast cancer based on MRI image data ( 99 - 103 ). In 2021, Liu et al evaluated the ability of a novel CNN architecture to predict 5-year cancer recurrence after MRI imaging of breast lesions.…”

Section: Resultsmentioning

confidence: 99%

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Use of artificial intelligence in breast surgery: a narrative review

Seth,

Lim,

Joseph

et al. 2024

Gland Surg

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Background and Objective We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.

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Section: Resultsmentioning

confidence: 99%

“…In 2021, Liu et al evaluated the ability of a novel CNN architecture to predict 5-year cancer recurrence after MRI imaging of breast lesions. The AI was able to identify image features relevant to prognostic outcomes and increased the accuracy of tumour classification ( 103 ).…”

Section: Resultsmentioning

confidence: 99%

Use of artificial intelligence in breast surgery: a narrative review

Seth,

Lim,

Joseph

et al. 2024

Gland Surg

View full text Add to dashboard Cite

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“…Given the limited size of data, we did not investigate relative importance of features and model parameters. We intend to do so in the future to gain possible key insights [29]. Finally, given the small sample size, the generalizability of our conclusions remains a limitation in our study.…”

Section: Discussionmentioning

confidence: 96%

Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma

et al. 2022

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Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem.

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