Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation

One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection : do we need more?

Purpose: To compare the performance of one-view digital breast tomosynthesis (1v-DBT) to that of three other protocols combining DBT and mammography (DM) for breast cancer detection. Materials and methods: Six radiologists, three experienced with 1v-DBT in screening, retrospectively reviewed 181 cases (76 malignant, 50 benign, 55 normal) in two sessions. First, they scored sequentially: 1v-DBT (medio-lateral oblique, MLO), 1v-DBT (MLO) + 1v-DM (cranio-caudal, CC) and two-view DM + DBT (2v-DM+2v-DBT). The second session involved only 2v-DM. Lesions were scored using BI-RADS® and level of suspiciousness (1–10). Sensitivity, specificity, receiver operating characteristic (ROC) and jack-knife alternative free-response ROC (JAFROC) were computed. Results: On average, 1v-DBT was non-inferior to any of the other protocols in terms of JAFROC figure-of-merit, area under ROC curve, sensitivity or specificity (p>0.391). While readers inexperienced with 1v-DBT screening improved their sensitivity when adding more images (69–79 %, p=0.019), experienced readers showed similar sensitivity (76 %) and specificity (70 %) between 1v-DBT and 2v-DM+2v-DBT (p=0.482). Subanalysis by lesion type and breast density showed no difference among modalities. Conclusion: Detection performance with 1v-DBT is not statistically inferior to 2v-DM or to 2v-DM+2v-DBT; its use as a stand-alone modality might be sufficient for readers experienced with this protocol. Key points: • One-view breast tomosynthesis is not inferior to two-view digital mammography.• One-view DBT is not inferior to 2-view DM plus 2-view DBT.• Training may lead to 1v-DBT being sufficient for screening

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography : Comparison With 101 Radiologists

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation

Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

Purpose: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. Methods and materials: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis. Results: Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (− 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > − 0.05) for any threshold except at the extreme AI score of 9. Conclusion: It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload. Key Points: • There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer. • The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists’ breast cancer detection performance. • When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography : Comparison With 101 Radiologists

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation

Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening

Objectives Breast cancer screening using magnetic resonance imaging (MRI) has limited accessibility due to high costs of breast MRI. Ultrafast dynamic contrast-enhanced breast MRI can be acquired within 2 minutes. We aimed to assess whether screening performance of breast radiologist using an ultrafast breast MRI-only screening protocol is as good as performance using a full multiparametric diagnostic MRI protocol (FDP). Materials and Methods The institutional review board approved this study, and waived the need for informed consent. Between January 2012 and June 2014, 1791 consecutive breast cancer screening examinations from 954 women with a lifetime risk of more than 20% were prospectively collected. All women were scanned using a 3 T protocol interleaving ultrafast breast MRI acquisitions in a full multiparametric diagnostic MRI protocol consisting of standard dynamic contrast-enhanced sequences, diffusion-weighted imaging, and T2-weighted imaging. Subsequently, a case set was created including all biopsied screen-detected lesions in this period (31 malignant and 54 benign) and 116 randomly selected normal cases with more than 2 years of follow-up. Prior examinations were included when available. Seven dedicated breast radiologists read all 201 examinations and 153 available priors once using the FDP and once using ultrafast breast MRI only in 2 counterbalanced and crossed-over reading sessions. Results For reading the FDP versus ultrafast breast MRI alone, sensitivity was 0.86 (95% confidence interval [CI], 0.81-0.90) versus 0.84 (95% CI, 0.78-0.88) (P = 0.50), specificity was 0.76 (95% CI, 0.74-0.79) versus 0.82 (95% CI, 0.79-0.84) (P = 0.002), positive predictive value was 0.40 (95% CI, 0.36-0.45) versus 0.45 (95% CI, 0.41-0.50) (P = 0.14), and area under the receiver operating characteristics curve was 0.89 (95% CI, 0.82-0.96) versus 0.89 (95% CI, 0.82-0.96) (P = 0.83). Ultrafast breast MRI reading was 22.8% faster than reading FDP (P < 0.001). Interreader agreement is significantly better for ultrafast breast MRI (κ = 0.730; 95% CI, 0.699-0.761) than for the FDP (κ = 0.665; 95% CI, 0.633-0.696). Conclusions Breast MRI screening using only an ultrafast breast MRI protocol is noninferior to screening with an FDP and may result in significantly higher screening specificity and shorter reading time

Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening

Objectives Breast cancer screening using magnetic resonance imaging (MRI) has limited accessibility due to high costs of breast MRI. Ultrafast dynamic contrast-enhanced breast MRI can be acquired within 2 minutes. We aimed to assess whether screening performance of breast radiologist using an ultrafast breast MRI-only screening protocol is as good as performance using a full multiparametric diagnostic MRI protocol (FDP). Materials and Methods The institutional review board approved this study, and waived the need for informed consent. Between January 2012 and June 2014, 1791 consecutive breast cancer screening examinations from 954 women with a lifetime risk of more than 20% were prospectively collected. All women were scanned using a 3 T protocol interleaving ultrafast breast MRI acquisitions in a full multiparametric diagnostic MRI protocol consisting of standard dynamic contrast-enhanced sequences, diffusion-weighted imaging, and T2-weighted imaging. Subsequently, a case set was created including all biopsied screen-detected lesions in this period (31 malignant and 54 benign) and 116 randomly selected normal cases with more than 2 years of follow-up. Prior examinations were included when available. Seven dedicated breast radiologists read all 201 examinations and 153 available priors once using the FDP and once using ultrafast breast MRI only in 2 counterbalanced and crossed-over reading sessions. Results For reading the FDP versus ultrafast breast MRI alone, sensitivity was 0.86 (95% confidence interval [CI], 0.81-0.90) versus 0.84 (95% CI, 0.78-0.88) (P = 0.50), specificity was 0.76 (95% CI, 0.74-0.79) versus 0.82 (95% CI, 0.79-0.84) (P = 0.002), positive predictive value was 0.40 (95% CI, 0.36-0.45) versus 0.45 (95% CI, 0.41-0.50) (P = 0.14), and area under the receiver operating characteristics curve was 0.89 (95% CI, 0.82-0.96) versus 0.89 (95% CI, 0.82-0.96) (P = 0.83). Ultrafast breast MRI reading was 22.8% faster than reading FDP (P < 0.001). Interreader agreement is significantly better for ultrafast breast MRI (κ = 0.730; 95% CI, 0.699-0.761) than for the FDP (κ = 0.665; 95% CI, 0.633-0.696). Conclusions Breast MRI screening using only an ultrafast breast MRI protocol is noninferior to screening with an FDP and may result in significantly higher screening specificity and shorter reading time