The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis

Ozsahin, Dilber Uzun; Emegano, Declan Ikechukwu; Uzun, Berna; Ozsahin, Ilker

doi:10.3390/diagnostics13010045

Cited by 23 publications

(13 citation statements)

References 96 publications

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“…Other trials investigated the use of AI in breast imaging and achieved better performances using XGBoost (28-31). This supervised learning algorithm is a gradient boosting algorithm that uses decision trees as its "weak" predictors and is known to have great prediction power (32). The tested performance of our model with 403 new samples was NPV 98.1% and PPV 77%.…”

Section: Discussionmentioning

confidence: 90%

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Buzatto,

Recife,

Miguel

et al. 2023

Preprint

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Purpose To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound and optimize the negative predictive value to minimize unnecessary biopsies. Methods We included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5 and 6 that underwent ultrasound guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset). We tested the performance of the final model with 403 new suspicious lesions. Results The most informative attributes were shape, margin, orientation and size of the lesions, the resistance index of the internal vessel, the age of the patient and the presence of a palpable lump. The highest mean NPV was achieved with KNN (97.9%). Making ensembles didn´t improve the performance. Tuning the threshold did improve the performance of the models and we chose the XGBoost with the tuned threshold as the final one. The tested performance of the final model was: NPV 98.1%, FN 1.9%, VPP 77.1%, FP 22.9%. Applying this final model, we would have missed 2 of the 231 malignant lesions of the test dataset (0.8%). Conclusion Machine learning can help physicians predict malignancy in suspicious breast lesions identified by the US. Our final model would be able to avoid 60.4% of the biopsies in benign lesions missing less than 1% of the cancer cases.

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Section: Discussionmentioning

confidence: 90%

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Buzatto,

Recife,

Miguel

et al. 2023

Preprint

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“…Due to our knowledge, this is the rst study evaluating the ChatGPT, an open AI, as a supportive tool for MDT discussing patients with a primary diagnosis of early breast cancer. The previous studies focused on AI in breast cancer mostly evaluated the application of AI in breast cancer screening and diagnosis [11]. McKinney could prove that participation of AI in the double-reeding process of mammography screening can reduce the workload of the second reader [12].…”

Section: Discussionmentioning

confidence: 99%

Evaluating ChatGPT as an Adjunct for the Multidisciplinary Tumor Board Decision-Making in Primary Breast Cancer Cases

Lukac

Dayan²,

Fink³

et al. 2023

Preprint

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As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer? We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated. Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1+ and 2+ (FISH negative) as in need of therapy with trastuzumab and called endocrine therapy “hormonal treatment”. Support of artificial intelligence by finding individualized and personalized therapy for our patients is unavoidable in this time of rapidly expanding amount of information. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.

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“…Specificity was the next highest performance metric at 99%. Most of the studies in this review originated in the United States, China, and Japan (Table 1 ) [ 44 ].…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection

Al Muhaisen,

Safi,

Ulayan

et al. 2024

Cureus

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Worldwide, breast cancer (BC) is one of the most commonly diagnosed malignancies in women. Early detection is key to improving survival rates and health outcomes. This literature review focuses on how artificial intelligence (AI), especially deep learning (DL), can enhance the ability of mammography, a key tool in BC detection, to yield more accurate results. Artificial intelligence has shown promise in reducing diagnostic errors and increasing early cancer detection chances. Nevertheless, significant challenges exist, including the requirement for large amounts of high-quality data and concerns over data privacy. Despite these hurdles, AI and DL are advancing the field of radiology, offering better ways to diagnose, detect, and treat diseases. The U.S. Food and Drug Administration (FDA) has approved several AI diagnostic tools. Yet, the full potential of these technologies, especially for more advanced screening methods like digital breast tomosynthesis (DBT), depends on further clinical studies and the development of larger databases. In summary, this review highlights the exciting potential of AI in BC screening. It calls for more research and validation to fully employ the power of AI in clinical practice, ensuring that these technologies can help save lives by improving diagnosis accuracy and efficiency.

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The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis

Cited by 23 publications

References 96 publications

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Evaluating ChatGPT as an Adjunct for the Multidisciplinary Tumor Board Decision-Making in Primary Breast Cancer Cases

Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection

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