Whole-Breast Ultrasound for Breast Screening and Archiving

Efficiency and impact factors of anatomical intelligence for breast and hand-held ultrasound in lesion detection

PURPOSE

To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection.

METHODS

A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed.

RESULTS

The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05).

CONCLUSIONS

With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.

The concordance in lesion detection and characteristics between the Anatomical Intelligence and conventional breast ultrasound Scan method

Background

The aim of this study was to investigate the concordance in lesion detection, between conventional Handhold Ultrasound (HHUS) and The Anatomical Intelligence for Breast ultrasound scan method.

Result

The AI-breast showed the absolute agreement between the resident and an experienced breast radiologist. The ICC for the scan time, number, clockface location, distance to the nipple, largest diameter and mean diameter of the lesion obtained by a resident and an experienced breast radiologist were 0.7642, 0.7692, 0.8651, 0.8436, 0.7502, 0.8885, respectively. The ICC of the both practitioners of AI-breast were 0.7971, 0.7843, 0.9283, 0.8748, 0.7248, 0.8163, respectively. The k value of Anatomical Intelligence breast between experienced breast radiologist and resident in these image characteristics of boundary, morphology, aspect ratio, internal echo, and BI-RADS assessment were 0.7424, 0.7217, 0.6741, 0.6419, 0.6241, respectively. The k value of the two readers of AI-breast were 0.6531, 0.6762, 0.6439, 0.6137, 0.5981, respectively.

Conclusion

The anatomical intelligent breast US scanning method has excellent reproducibility in recording the lesion location and the distance from the nipple, which may be utilized in the lesions surveillance in the future.

Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation

BACKGROUND AND OBJECTIVE

Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method.

METHODS

WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared.

RESULTS

The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy (ACC) was 0.963±0.054. Sensitivity (SENS) was 0.928±0.11. Specificity (SPEC) was 0.967±0.054. Dice coefficient (Dice) was 0.916±0.98. Region intersection over union (IoU) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area (R = 0.843, R= 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images.

CONCLUSIONS

Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.

Diagnostic value of an automated breast volume scanner compared with a hand-held ultrasound: a meta-analysis

Background

The diagnostic performance of an automated breast volume scanner (ABVS) compared with that of a hand-held ultrasound (HHUS) for breast cancer remains unclear. We performed a meta-analysis to compare the diagnostic performances of the ABVS and HHUS for breast cancer.

Methods

We searched PubMed, EMBASE, Cochrane, and SinoMed databases to identify eligible studies up until November 14, 2018. Studies comparing ABVS and HHUS for differentiating benign and malignant breast tumors were included. A meta-analysis was performed to generate pooled diagnostic accuracy parameters [sensitivity, specificity, diagnostic odds ratio (DOR), area under the curve (AUC), and the Q* index] and detection rates for ABVS and HHUS.

Results

Nine studies involving 1,376 patients and 1,527 lesions were included in the meta-analysis for diagnostic accuracy. The pooled sensitivity was 0.93 [95% confidence interval (CI), 0.91-0.95] for ABVS and 0.90 (95% CI, 0.88-0.92) for HHUS, and the pooled specificity was 0.86 (95% CI, 0.83-0.88) for ABVS and 0.82 (95% CI, 0.79-0.84) for HHUS. The pooled DOR was 88.66 (95% CI, 51.44-152.78) for ABVS and 41.06 for HHUS (95% CI, 26.58-63.42). The AUC of the summary receiver operating characteristic (SROC) was 0.9496 for ABVS and 0.9143 for HHUS, and the Q* index was 0.8899 for ABVS and 0.8469 for HHUS. Meta-regression showed no significant difference between the diagnostic accuracy of ABVS and HHUS (P=0.0771). No publication bias was found. Thirteen published studies involving 1,047 pathologically confirmed malignant lesions were included to generate a pooled detection rate. The pooled detection rate was 1.00 (95% CI, 1.00-1.00) for both ABVS and HHUS, for which a publication bias was found.

Conclusions

ABVS can be used as an appropriate screening tool for breast cancer as well as HHUS in diagnostic accuracy and detection rate. Considering other advantages of ABVS including non-radioactivity, sensitivity to dense breast, three-dimensional reconstruction, time-saving and repeatability, it might be a promising screening tool for young or dense-breast women in the future.

Freehand 3-D Ultrasound Imaging: A Systematic Review

Two-dimensional ultrasound (US) imaging has been successfully used in clinical applications as a low-cost, portable and non-invasive image modality for more than three decades. Recent advances in computer science and technology illustrate the promise of the 3-D US modality as a medical imaging technique that is comparable to other prevalent modalities and that overcomes certain drawbacks of 2-D US. This systematic review covers freehand 3-D US imaging between 1970 and 2017, highlighting the current trends in research fields, the research methods, the main limitations, the leading researchers, standard assessment criteria and clinical applications. Freehand 3-D US systems are more prevalent in the academic environment, whereas in clinical applications and industrial research, most studies have focused on 3-D US transducers and improvement of hardware performance. This topic is still an interesting active area for researchers, and there remain many unsolved problems to be addressed.