Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists

Nicosia, Luca; Addante, Francesca; Bozzini, Anna Carla; Latronico, Antuono; Montesano, Marta; Meneghetti, Lorenza; Tettamanzi, Francesca; Frassoni, Samuele; Bagnardi, Vincenzo; Santis, Rossella De; Pesapane, Filippo; Fodor, C.; Mastropasqua, Mauro G.; Cassano, Enrico

doi:10.1016/j.clinimag.2021.11.006

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…In four studies 19 , 20 , 24 , 32 , DL exhibited higher specificity than overall human readers. In another study 26 , DL was more specific than less experienced human readers but less specific than experienced human readers. The remaining studies did not report comparable diagnostic measures between DL systems and human readers.…”

Section: Resultsmentioning

confidence: 88%

“…Overall, as shown in Fig. 2, 16 studies [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] were ultimately included in this review, according to inclusion criteria. In addition, based on the PICO framework (population, intervention, comparison, outcome), exclusions and the corresponding reasons after full-text review were presented in Supplementary Tables 1 and 2.…”

Section: Study Selection and Study Characteristicsmentioning

confidence: 99%

“…These studies comprised 9238 women in total, of which 3 studies [30][31][32] recruited 901, 582, and 5012 female participants respectively, the remaining 13 studies [17][18][19][20][21][22][23][24][25][26][27][28][29] included smaller numbers of women (from 40 to 472). Seven studies evaluated data from China 19,21,23,27,29,30,32 , 6 studies enrolled participants from Korea 17,18,20,24,25,28 , 2 from Italy 22,26 , and the remaining 1 study 31 used public multisite data from which the countries were not reported. Of all studies, 15 were conducted in a diagnostic setting, while the remaining 1 was evaluated in a screening setting 18 .…”

Section: Study Selection and Study Characteristicsmentioning

confidence: 99%

“…Of all studies, 15 were conducted in a diagnostic setting, while the remaining 1 was evaluated in a screening setting 18 . All included studies employed DL convolutional neural networks, of which 14 were commercial DL systems, including S-Detect [17][18][19][20][21][22][23][24][25][26][28][29][30] and BU-CAD 27 , and 2 were investigatorderived DL systems 31,32 . In addition, there were 6 studies 17,18,24-28 using retrospective US images to compare the diagnostic accuracy of DL systems and human readers.…”

Section: Study Selection and Study Characteristicsmentioning

confidence: 99%

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Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review

Dan,

Xu,

Burrows

et al. 2024

npj Precis. Onc.

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Deep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.

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Section: Resultsmentioning

confidence: 88%

Section: Study Selection and Study Characteristicsmentioning

confidence: 99%

Section: Study Selection and Study Characteristicsmentioning

confidence: 99%

Section: Study Selection and Study Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review

Dan,

Xu,

Burrows

et al. 2024

npj Precis. Onc.

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show abstract

“…In the precision medicine context, quantitated methods provide the unique potential for making breast cancer screening more rapid and accurate using artificial intelligence and machine learning algorithms ( 13 ). Many studies are evaluating the applicability of US prediction models that are based on quantitated methods (e.g., radiomics) ( 14 – 17 ).…”

Section: Introductionmentioning

confidence: 99%

Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study

Zhang

Liu

et al. 2022

Front. Oncol.

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BackgroundWith advances in high-throughput computational mining techniques, various quantitative predictive models that are based on ultrasound have been developed. However, the lack of reproducibility and interpretability have hampered clinical use. In this study, we aimed at developing and validating an interpretable and simple-to-use US nomogram that is based on quantitative morphometric features for the prediction of breast malignancy.MethodsSuccessive 917 patients with histologically confirmed breast lesions were included in this retrospective multicentric study and assigned to one training cohort and two external validation cohorts. Morphometric features were extracted from grayscale US images. After feature selection and validation of regression assumptions, a dynamic nomogram with a web-based calculator was developed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness.ResultsThrough feature selection, three morphometric features were identified as being the most optimal for predicting malignancy, and all regression assumptions of the prediction model were met. Combining all these predictors, the nomogram demonstrated a good discriminative performance in the training cohort and in the two external validation cohorts with AUCs of 0.885, 0.907, and 0.927, respectively. In addition, calibration and decision curves analyses showed good calibration and clinical usefulness.ConclusionsBy incorporating US morphometric features, we constructed an interpretable and easy-to-use dynamic nomogram for quantifying the probability of breast malignancy. The developed nomogram has good generalization abilities, which may fit into clinical practice and serve as a potential tool to guide personalized treatment. Our findings show that quantitative morphometric features from different ultrasound machines and systems can be used as imaging surrogate biomarkers for the development of robust and reproducible quantitative ultrasound dynamic models in breast cancer research.

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YOLOv8 Outperforms Traditional CNN Models in Mammography Classification: Insights From a Multi‐Institutional Dataset

AkbarnezhadSany,

EntezariZarch,

AlipoorKermani

et al. 2024

Int J Imaging Syst Tech

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This study evaluates the efficacy of four deep learning methods—YOLOv8, VGG16, ResNet101, and EfficientNet—for classifying mammography images into normal, benign, and malignant categories using a large‐scale, multi‐institutional dataset. Each dataset was divided into training and testing groups with an 80%/20% split, ensuring that all examinations from the same patient were consistently allocated to the same split. The training set for the malignant class contained 10 220 images, the benign class 6086 images, and the normal class 8526 images. For testing, the malignant class had 1441 images, the benign class 1124 images, and the normal class 1881 images. All models were fine‐tuned using transfer learning and standardized to 224 × 224 pixels with data augmentation techniques to improve robustness. Among the models, YOLOv8 demonstrated the highest performance, achieving an AUC of 93.33% for the training dataset and 91% for the testing dataset. It also exhibited superior accuracy (91.82% training, 86.68% testing), F1‐score (91.11% training, 84.86% testing), and specificity (95.80% training, 93.32% testing). ResNet101, VGG16, and EfficientNet also performed well, with ResNet101 achieving an AUC of 91.67% (training) and 90.00% (testing). Grad‐CAM visualizations were used to identify the regions most influential in model decision‐making. This multi‐model evaluation highlights YOLOv8's potential for accurately classifying mammograms, while demonstrating that all models contribute valuable insights for improving breast cancer detection. Future clinical trials will focus on refining these models to assist healthcare professionals in delivering accurate and timely diagnoses.

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Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists

Cited by 13 publications

References 20 publications

Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review

Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review

Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study

YOLOv8 Outperforms Traditional CNN Models in Mammography Classification: Insights From a Multi‐Institutional Dataset

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