Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets

BACKGROUND AND AIMS

It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets.

METHODS

Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892).

RESULTS

We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity.

CONCLUSIONS

Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.

Deep learning-based classification of breast lesions using dynamic ultrasound video

PURPOSE

We intended to develop a deep-learning-based classification model based on breast ultrasound dynamic video, then evaluate its diagnostic performance in comparison with the classic model based on ultrasound static image and that of different radiologists.

METHOD

We collected 1000 breast lesions from 888 patients from May 2020 to December 2021. Each lesion contained two static images and two dynamic videos. We divided these lesions randomly into training, validation, and test sets by the ratio of 7:2:1. Two deep learning (DL) models, namely DL-video and DL-image, were developed based on 3D Resnet-50 and 2D Resnet-50 using 2000 dynamic videos and 2000 static images, respectively. Lesions in the test set were evaluated to compare the diagnostic performance of two models and six radiologists with different seniority.

RESULTS

The area under the curve of the DL-video model was significantly higher than those of the DL-image model (0.969 vs. 0.925, P = 0.0172) and six radiologists (0.969 vs. 0.779-0.912, P < 0.05). All radiologists performed better when evaluating the dynamic videos compared to the static images. Furthermore, radiologists performed better with increased seniority both in reading images and videos.

CONCLUSIONS

The DL-video model can discern more detailed spatial and temporal information for accurate classification of breast lesions than the conventional DL-image model and radiologists, and its clinical application can further improve the diagnosis of breast cancer.

Improving breast cancer diagnostics with deep learning for MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set (n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference (P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists' performance improved when their predictions were averaged with DL's predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study

PURPOSE

To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.

METHODS

Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites.

RESULTS

A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157).

CONCLUSIONS

Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.

Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.

Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy

PURPOSE

To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies.

METHODS

Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards.

RESULTS

Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P <  0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P <  0.05, compared with conventional US and stratification A).

CONCLUSION

Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.