Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography

Ah, Yoon; Jang, Mijung; Yun, Bo La; Shin, Sung Ui; Kim, Bohyoung; Kim, Sun Mi

doi:10.3390/diagnostics11081409

Cited by 17 publications

(7 citation statements)

References 31 publications

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“…Using another AI system, the probability of malignancy was retrospectively visually categorized by radiologists and lesion-specifically compared with the AI system at a 10 % threshold. There was no significant difference between the area under the receiver-operator-characteristic-curve (AUC) regarding malignancy scores and categorizations between readers and AI 15 . The results are in line with previous work, stating that neural networks can achieve over 98 % accuracy in categorizing suspicious calcifications 16 .…”

Section: Discussionmentioning

confidence: 98%

“…The histological complexity of calcification-associated lesions might be the reason [7,17]. Clustered amorphous calcifications of category 4a are most common among invasive assessments of calcifications [15].…”

Section: Discussionmentioning

confidence: 99%

“…Unter Verwendung eines anderen KI-CAD-Systems wurde retrospektiv durch Radiologen die Wahrscheinlichkeit für Malignität visuell kategorisiert und läsionen-spezifisch mit einem Cutt-off von 10 % mit der KI-Bewertung verglichen. Die Studie fand keinen signifikanten Unterschied zwischen den AUC-(area under the receiver operator characteristic curve) Werten bezüglich Malignitäts-Scores und Kategorisierungen zwischen Befundern und KI [15]. Die Ergebnisse stehen im Einklang damit, dass neuronale Netzwerke eine Genauigkeit in der suspekten Mikrokalkkategorisierung von über 98 % erreichen können [16].…”

Section: Diskussionunclassified

“…Die histologische Komplexität der Mikrokalk-assoziierten Läsionen mit einer teils mammografisch unspezifischen Mikrokalkproduktion mag ursächlich sein [7,17]. Amorphe, gruppierte Mikroverkalkungen der Befundstufe 4a prägen die invasive Mikrokalkabklärung [15].…”

Section: Diskussionunclassified

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Artificial Intelligence for Indication of Invasive Assessment of Calcifications in Mammography Screening

Weigel

Brehl²,

Heindel

et al. 2023

Rofo

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Purpose Lesion-related evaluation of the diagnostic performance of an individual artificial intelligence (AI) system to assess mamographically detected and histologically proven calcifications. Materials and Methods This retrospective study included 634 women of one screening unit (July 2012 – June 2018) who completed the invasive assessment of calcifications. For each leasion, the AI-system calculated a score between 0 and 98. Lesions scored > 0 were classified as AI-positive. The performance of the system was evaluated based on its positive predictive value of invasive assessment (PPV3), the false-negative rate and the true-negative rate. Results The PPV3 increased across the categories (readers: 4a: 21.2 %, 4b: 57.7 %, 5: 100 %, overall 30.3 %; AI: 4a: 20.8 %, 4b: 57.8 %, 5: 100 %, overall: 30.7 %). The AI system yielded a false-negative rate of 7.2 % (95 %-CI: 4.3 %: 11.4 %) and a true-negative rate of 9.1 % (95 %-CI: 6.6 %; 11.9 %). These rates were highest in category 4a, 12.5 % and 10.4 % retrospectively. The lowest median AI score was observed for benign lesions (61, interquartile range (IQR): 45–74). Invasive cancers yielded the highest median AI score (81, IQR: 64–86). Median AI scores for ductal carcinoma in situ were: 74 (IQR: 63–84) for low grade, 70 (IQR: 52–79) for intermediate grade and 74 (IQR: 66–83) for high grade. Conclusion At the lowest threshold, the AI system yielded calcification-related PPV3 values that increased across categories, similar as seen in human evaluation. The strongest loss in AI-based breast cancer detection was observed for invasively assessed calcifications with the lowest suspicion of malignancy, yet with a comparable decrease in the false-positive rate. An AI-score based stratification of malignant lesions could not be determined. Key Points: Citation Format

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Diskussionunclassified

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Artificial Intelligence for Indication of Invasive Assessment of Calcifications in Mammography Screening

Weigel

Brehl²,

Heindel

et al. 2023

Rofo

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“…Machine learning (ML) is rapidly progressing in many scientific fields, including medicine. 1 For breast cancer, we are beholding the emergence of commercially available artificial intelligence (AI) systems for breast cancer detection (AI-CADe), 2 – 8 triaging, diagnosis, and risk assessment. 9 If the performance of such systems proves to be accurate and robust in a clinical setting, incorporating them into the screening process can significantly benefit both the hospital and the screening participants.…”

Section: Introductionmentioning

confidence: 99%

VAI-B: a multicenter platform for the external validation of artificial intelligence algorithms in breast imaging

Cossío

Schurz

Engström³

et al. 2023

J. Med. Imag.

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Purpose: Multiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data. Approach:We developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data onpremises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes.Results: To pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database. Conclusions:We have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.

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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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Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography

Cited by 17 publications

References 31 publications

Artificial Intelligence for Indication of Invasive Assessment of Calcifications in Mammography Screening

Artificial Intelligence for Indication of Invasive Assessment of Calcifications in Mammography Screening

VAI-B: a multicenter platform for the external validation of artificial intelligence algorithms in breast imaging

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

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