Deformable mapping using biomechanical models to relate corresponding lesions in digital breast tomosynthesis and automated breast ultrasound images

This work investigates the application of a deformable localization/mapping method to register lesions between the digital breast tomosynthesis (DBT) craniocaudal (CC) and mediolateral oblique (MLO) views and automated breast ultrasound (ABUS) images. This method was initially validated using compressible breast phantoms. This methodology was applied to 7 patient data sets containing 9 lesions. The automated deformable mapping algorithm uses finite element modeling and analysis to determine corresponding lesions based on the distance between their centers of mass (d_COM) in the deformed DBT model and the reference ABUS model. This technique shows that location information based on external fiducial markers is helpful in the improvement of registration results. However, use of external markers are not required for deformable registration results described by this methodology. For DBT (CC view) mapped to ABUS, the mean d_COM was 14.9 ± 6.8 mm based on 9 lesions using 6 markers in deformable analysis. For DBT (MLO view) mapped to ABUS, the mean d_COM was 13.7 ± 6.8 mm based on 8 lesions using 6 markers in analysis. Both DBT views registered to ABUS lesions showed statistically significant improvements (p ≤ 0.05) in registration using the deformable technique in comparison to a rigid registration. Application of this methodology could help improve a radiologist's characterization and accuracy in relating corresponding lesions between DBT and ABUS image datasets, especially for cases of high breast densities and multiple masses.     

Whole-Breast Ultrasound for Breast Screening and Archiving

The incidence of breast cancer is increasing worldwide, reinforcing the importance of breast screening. Conventional hand-held ultrasound (HHUS) for breast screening is efficient and relatively easy to perform; however, it lacks systematic recording and localization. This study investigated an electromagnetic tracking-based whole-breast ultrasound (WBUS) system to facilitate the use of HHUS for breast screening. One-hundred nine breast masses were collected, and the detection of suspicious breast lesions was compared between the WBUS system, HHUS and a commercial automated breast ultrasound (ABUS) system. The positioning error between WBUS and ABUS (1.39 ± 0.68 cm) was significantly smaller than that between HHUS and ABUS (1.62 ± 0.91 cm, p = 0.014) and HHUS and WBUS (1.63 ± 0.9 cm, p = 0.024). WBUS is a practical clinical tool for breast screening that can be used instead of the often unavailable and costly ABUS.     

Diagnostic Performance of Automated Breast Ultrasound as a Replacement for a Hand-Held Second-Look Ultrasound for Breast Lesions Detected Initially on Magnetic Resonance Imaging

To evaluate the diagnostic performance of automated breast ultrasound (ABUS) after breast magnetic resonance imaging (MRI) as a replacement for hand-held second-look ultrasound (HH-SLUS), we evaluated 58 consecutive patients with breast cancer who had additional suspicious lesions on breast MRI. All patients underwent HH-SLUS and ABUS. Three breast radiologists evaluated the detectability, location, characteristics and conspicuity of lesions on ABUS. We also evaluated inter-observer variability and compared the results with HH-SLUS results. Eighty additional suspicious lesions were identified on breast MRI. Fifteen of the 80 lesions (19%) were not detected on HH-SLUS. Eight of the 15 lesions (53%) were detected on ABUS, whereas the remaining 7 were not detected on ABUS. Among the 65 lesions detected on HH-SLUS, only 3 lesions were not detected on ABUS. The intra-class correlation coefficients for lesion location and size all exceeded 0.70, indicating high reliability. Moderate to fair agreement was found for mass shape, orientation, margin and Breast Imaging Reporting and Data System (BI-RADS) final assessment. Therefore, ABUS can reliably detect additional suspicious lesions identified on breast MRI and may help in the decision on biopsy guidance method (US vs. MRI) as a replacement tool for HH-SLUS.     

MRI to X-ray mammography registration using a volume-preserving affine transformation

X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.     

几何模型匹配乳腺头足位与内外斜位X线片所示病灶

目的 观察几何模型(GM)匹配乳腺头足(CC)位与内外斜(MLO)位X线片所示病灶的价值。方法 回顾性分析493例接受乳腺CC位和MLO位X线摄影的乳腺病灶患者,共598个乳腺病灶,包括499个钙化灶和99个肿块。构建GM用于匹配CC与MLO位片所示乳腺病灶,再以环形法(AB)和直线法(SS)进行对比,分别计算匹配误差,包括GM匹配误差、AB径向误差及SS轴向误差;分析GM对CC及MLO位图像中同一病灶的匹配性能,评价其应用价值。结果 GM对乳腺钙化灶和肿块的匹配误差分别为2.85(1.45,5.08)及3.70(1.35,6.25)mm,差异无统计学意义(Z=-1.344,P=0.179)。对乳腺上部病灶,AB匹配的径向误差和SS匹配的轴向误差均大于下部病灶(P均<0.001);对乳腺外侧病灶,AB的径向误差和SS的轴向误差均大于内侧病灶(P均<0.05)。GM、AB及SS间匹配误差整体差异有统计学意义(H=93.012,P<0.001);两两比较差异均有统计学意义(P均<0.05),GM匹配性能明显优于AB和SS。GM匹配误差与摄片时乳腺压迫厚度无明显相关性...     

Diagnostic Performance Using Automated Breast Ultrasound System for Breast Cancer in Chinese Women Aged 40 Years or Older: A Comparative Study

The purpose of this study was to investigate the diagnostic performance of the automated breast ultrasound system (ABUS) compared with hand-held ultrasonography (HHUS) and mammography (MG) for breast cancer in women aged 40 y or older. A total of 594 breasts in 385 patients were enrolled in the study. HHUS, ABUS and MG exams were performed for these patients. Follow-up and pathologic findings were used as the reference standard. Based on the reference standard, 519 units were benign or normal and 75 were malignant. The sensitivity, specificity, accuracy and Youden index were 97.33%, 89.79%, 90.74% and 0.87 for HHUS; 90.67%, 92.49%, 92.26% and 0.83 for ABUS; 84.00%, 92.87%, 91.75% and 0.77 for MG, respectively. The specificity of ABUS was significantly superior to that of HHUS (p = 0.024). The area under the receiver operating characteristic curve was 0.936 for HHUS, which was the highest, followed by 0.916 for ABUS and 0.884 for MG. However, the difference was not statistically significant (p > 0.05). In conclusion, the diagnostic performance of ABUS for breast cancer was equivalent to HHUS and MG and potentially can be used as an alternative method for breast cancer diagnosis.     

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign). An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five-folder cross-validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning-based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG), our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers’ AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.     

Local diagnostic reference levels (LDRLs) for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) procedures in Morocco

IntroductionThe objective of this study was to establish local diagnostic reference levels (LDRLs) for the full-field digital mammography (FFDM) and tomosynthesis (DBT) in Moroccan health facilities.MethodsData from 146 women were collected from three facilities. The proposed DRLs were defined as the 75th percentile of the mean average glandular dose (AGD) distribution.ResultsThe mean AGD recorded in this study for the three centers was 1.47 mGy for all centers, and 1.42 mGy and 1.64 mGy for the CC and MLO projections, respectively. The mean compressed breast thickness (CBT) values recorded in this current study were 55 mm, the LDRLs reported for all centers was 1.7 mGy, the CC projection was 1.6 mGy, and the MLO projection was 1.8 mGy. In addition, the LDRLs reported in the current study were compared with those from previous studies for other countries, including the United Kingdom, Japan, Ghana, and Sri Lanka.ConclusionThis work provides an assessment of local DRLs for mammography in Morocco and is suggested as a starting point that will allow professionals to evaluate and optimize their practice. Furthermore, the definition of national DRLs is a necessary process in optimizing Moroccan medical exposures.     

Evaluation of Tissue Stiffness Around Lesions by Sound Touch Shear Wave Elastography in Breast Malignancy Diagnosis

The aim of the study described here was to assess the evaluation of tissue stiffness around lesions by sound touch shear wave elastography (STE) in breast malignancy diagnosis. This was an institutional ethics committee–approved, single-center study. A total of 90 women with breast masses examined with conventional ultrasound and STE were eligible for enrollment from December 2020 to July 2021. The maximum and mean elastic values of masses, E_max and E_mean, were determined. Shell function was used to measure the maximum and mean elastic values of tissues around masses in annular shells 0.5, 1.0, 1.5 and 2.0 mm wide, recorded as corresponding E_max-shell and E_mean-shell. All parameters were analyzed and compared with histopathologic results. Receiver operating characteristic curves were constructed to assess diagnostic performance. Logistic regression analysis was conducted to determine the best diagnostic model. Collagen fiber content of tissues around breast lesions was evaluated using Masson staining and ImageJ software. Ninety women with breast masses were included in this study; 50 had benign (mean diameter 15.84 ± 4.39 mm) and 40 had malignant (mean diameter 17.40 ± 5.42 mm) masses. The diagnostic value of E_{max-shell-2.0} was the highest (area under the curve = 0.930) with a sensitivity of 87.5% and specificity of 88%. According to stepwise logistic regression analysis, E_{max-shell-2.0} and age were independent predictors of malignancy. E_{max-shell-2.0} was also found to be highly correlated with the collagen fiber content of tissue in the malignant group (r = 0.877). Tissue stiffness around lesions measured by STE is a useful metric in identifying malignant breast masses by reflecting collagen fiber content, and E_{max-shell-2.0} performs best.     

Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images

Transorbital sonography provides reliable information about the estimation of intra-cranial pressure by measuring the optic nerve sheath diameter (ONSD), whereas the optic nerve (ON) diameter (OND) may reveal ON atrophy in patients with multiple sclerosis. Here, an AUTomatic Optic Nerve MeAsurement (AUTONoMA) system for OND and ONSD assessment in ultrasound B-mode images based on deformable models is presented. The automated measurements were compared with manual ones obtained by two operators, with no significant differences. AUTONoMA correctly segmented the ON and its sheath in 71 out of 75 images. The mean error compared with the expert operator was 0.06 ± 0.52 mm and 0.06 ± 0.35 mm for the ONSD and OND, respectively. The agreement between operators and AUTONoMA was good and a positive correlation was found between the readers and the algorithm with errors comparable with the inter-operator variability. The AUTONoMA system may allow for standardization of OND and ONSD measurements, reducing manual evaluation variability.     

Automated extraction,labelling and analysis of the coronary vasculature from arteriograms

For clinical decision-making and documentation purposes we have developed techniques to extract, label and analyze the coronary vasculature from arteriograms in an automated, quantitative manner. Advanced image processing techniques were applied to extract and analyze the vasculatures from non-subtracted arteriograms while artificial intelligence techniques were employed to assign anatomical labels. Lumen diameters of 11 phantom vessels were assessed with an accuracy of 0.27±0.19 mm (d _true = 0.45 + 0.92d _measured ; r> 0.99) and 0.21±0.15 mm (d _true =0.42+0.91d _measured ; r> 0.99), from cine and digital images, respectively. We collected a total of 15 routinely acquired cine-arteriograms showing 74 vessel segments with 18 stenoses (severity larger than 30% assessed quantitatively), and 53 digital arteriograms showing 236 vessel segments with 69 stenoses. From the cine arteriograms we extracted 64 (86%) of the vessel segments without manual correction and 196 (83%) from the digital arteriograms. Repeated analysis (3 times) of the arteriograms by the same operator resulted in a standard deviation of the mean segment diameters (precision) of 0.064 mm for the cine-images and 0.020 mm for the digital images, while the standard deviations in the measurement of the minimal luminal diameter of the observed stenoses were 0.020 mm and 0.019 mm, respectively. The LAD artery, the septal and diagonal branches were correctly identified automatically in 86% of the segments. From these evaluations we conclude that our automated approach provides reliable tools for the assessment of multi-vessel disease, both in an offand on-line environment.     

Computerized Detection of Breast Lesions Using Deformable Part Models in Ultrasound Images

Ultrasound imaging is considered an important complementary technique for the screening of dense breasts. Detection of lesions at an early stage is a key step in which computerized lesion detection systems could play an important role in the analysis of US images. In this article, we propose adaptation of a generic object detection technique, deformable part models, to detect lesions in breast US images. The data set used in this study included 326 images, all from different patients (54 malignant lesions, 109 benign lesions and 163 healthy breasts). In terms of lesion detection, our proposal outperformed some of the most relevant approaches described in the literature; we obtained a sensitivity of 86% with 0.28 false-positive detection per image and an A_z value of 0.975. In the detection of malignant lesions, our proposed approached had an A_z value of 0.93 and a sensitivity of 78% at a 1.15 false-positive detections per image.     

Blood Flow and Glucose Metabolism in Stage IV Breast Cancer: Heterogeneity of Response During Chemotherapy

Objective  The purpose of the study was to compare early changes in blood flow (BF) and glucose metabolism (MR_glu) in metastatic breast cancer lesions of patients treated with chemotherapy. Methods  Eleven women with stage IV cancer and lesions in breast, lymph nodes, liver, and bone were scanned before treatment and after the first course of chemotherapy. BF, distribution volume of water (V _d), MR_glu/BF ratio, MR_glu and its corresponding rate constants K ₁ and k ₃ were compared per tumor lesion before and during therapy. Results  At baseline, mean BF and MR_glu varied among different tumor lesions, but mean V _d was comparable in all lesions. After one course of chemotherapy, mean MR_glu decreased in all lesions. Mean BF decreased in breast and node lesions and increased in bone lesions. V _d decreased in breast and nodes, but did not change in bone lesions. The MR_glu/BF ratio decreased in breast and bone lesions and increased in node lesions. In patients with multiple tumor lesions BF and MR_glu response could be very heterogeneous, even within similar types of metastases. BF and MR_glu increased in lesions of patients who experienced early disease progression or showed no response during clinical follow-up. Conclusion  BF and MR_glu changes separately give unique information on different aspects of tumor response to chemotherapy. Changes in BF and MR_glu parameters can be remarkably heterogeneous in patients with multiple lesions.     

ABUS examination time: An observational study of operators’ experience

ObjectiveIn this study we aimed to evaluate the operation times of ABUS by technologists during the learning time course and share the learning experience.Materials and methodThe first consequent 400 examinations after the installation of an ABUS unit in the breast clinic between August 2017 and December 2017 were included. Total examination time was measured for each procedure. The initial and final examination times during the learning period were compared. Data were analyzed with the Mann-Whitney Test.ResultsThe acquisition times for routine six position examination ranged between eight and 36 minutes with an average of 13.2 ± 3.58 min. The examination time for the eight position examination ranged between 18 and 32 min, with an average of 22.9 ± 3.93 min. The overall average examination time was 13.3 ± 3.98 min. There was a significant difference (p = 0.00) between the average initial and final examination times of the learning period with an average decrease of 10.6 min.ConclusionThe average time of an ABUS examination for an average breast is less than 15 min. ABUS examination time reduced as technologists became familiar with the sonographic anatomy of the breast and experienced in positioning technique during the learning curve.     

A Novel Framework for Automated Segmentation and Labeling of Homogeneous <Emphasis Type="Italic">Versus</Emphasis> Heterogeneous Lung Tumors in [<Superscript>18</Superscript>F]FDG-PET Imaging

Purpose

Determination of intra-tumor high-uptake area using 2-deoxy-2-[¹⁸F]fluoro-d-glucose ([¹⁸F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [¹⁸F]FDG-PET with the capability of intra-tumor high-uptake region delineation.

Procedures

We utilized and extended a fuzzy random walk PET tumor segmentation algorithm to delineate intra-tumor high-uptake areas. Tumor textural feature (TF) analysis was used to find a relationship between tumor type and TF values. Segmentation accuracy was evaluated quantitatively utilizing 70 clinical [¹⁸F]FDG-PET lung images of patients with a total of 150 solid tumors. For volumetric analysis, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) measures were extracted with respect to gold-standard manual segmentation. A multi-linear regression model was also proposed for automated tumor labeling based on TFs, including cross-validation analysis.

Results

Two-tailed t test analysis of TFs between homogeneous and heterogeneous tumors revealed significant statistical difference for size-zone variability (SZV), intensity variability (IV), zone percentage (ZP), proposed parameters II and III, entropy and tumor volume (p < 0.001), dissimilarity, high intensity emphasis (HIE), and SUV_min (p < 0.01). Lower statistical differences were observed for proposed parameter I (p = 0.02), and no significant differences were observed for SUV_max and SUV_mean. Furthermore, the Spearman rank analysis between visual tumor labeling and TF analysis depicted a significant correlation for SZV, IV, entropy, parameters II and III, and tumor volume (0.68 ≤ ρ ≤ 0.84) and moderate correlation for ZP, HIE, homogeneity, dissimilarity, parameter I, and SUV_min (0.22 ≤ ρ ≤ 0.52), while no correlations were observed for SUV_max and SUV_mean (ρ < 0.08). The multi-linear regression model for automated tumor labeling process resulted in R ² and RMSE values of 0.93 and 0.14, respectively (p < 0.001), and generated tumor labeling sensitivity and specificity of 0.93 and 0.89. With respect to baseline random walk segmentation, the results showed significant (p < 0.001) mean DSC, HD, and SUV_mean error improvements of 21.4 ± 11.5 %, 1.4 ± 0.8 mm, and 16.8 ± 8.1 % in homogeneous tumors and 7.4 ± 4.4 %, 1.5 ± 0.6 mm, and 7.9 ± 2.7 % in heterogeneous lesions. In addition, significant (p < 0.001) mean DSC, HD, and SUV_mean error improvements were observed for tumor sub-volume delineations, namely 5 ± 2 %, 1.5 ± 0.6 mm, and 7 ± 3 % for the proposed Fuzzy RW method compared to RW segmentation.

Conclusion

We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [¹⁸F]FDG-PET images.

     

不同年资医师应用自动乳腺超声诊断系统结合CAD对乳腺恶性病灶的诊断价值

目的探讨不同年资医师应用计算机辅助诊断系统(CAD)辅助自动乳腺超声诊断系统(ABUS)对于诊断乳腺恶性病灶的价值。方法收集行ABUS检查的乳腺病灶患者1452例,其中,恶性270例,共282个病灶;良性674例,共695个病灶;阴性508例。比较6名医师(3名低年资医师与3名高年资医师)使用CAD系统前后的诊断敏感性、特异性、受试者工作特征(ROC)曲线下面积及平均阅读时间。结果应用CAD前,低年资医师与高年资医师诊断恶性病灶的敏感性分别为87%、93%,使用CAD后均提高至94%,低年资医师使用CAD前、后诊断敏感性比较差异有统计学意义(P<0.05),高年资医师差异无统计学意义。6名医师在使用CAD系统前后诊断特异性无变化。低年资医师在使用CAD系统后的诊断准确率有所提高,曲线下面积由0.85提高至0.89,差异有统计学意义(P<0.05);而高年资医师在使用CAD系统后,虽然ROC曲线下面积由0.91提高至0.92,但差异无统计学意义。所有医师使用CAD后的平均阅读时间均有不同程度的延长,差异有统计学意义(P<0.05)。结论虽然使用CAD后的平均阅读时间有所延长,但在可接受范围内,ABUS结合CAD能大大提高超声医师诊断乳腺恶性病灶的准确率和敏感性,且对低年资医师帮助更大。     

Determination of the Elasticity of Breast Tissue during the Menstrual Cycle Using Real-Time Shear Wave Elastography

We examined breast tissue elasticity during the menstrual cycle using real-time shear wave elastography (RT-SWE), a recent technique developed for soft tissue imaging. Written informed consent for RT-SWE was obtained from all eligible patients, who were healthy women aged between 19 and 52 y. Young's moduli of the breast tissue in the early follicular, late phase and luteal phase were compared. There were no significant differences in the mean, maximum and minimum elasticity values (E_mean, E_max and E_min) and standard deviation (E_SD). RT-SWE of glandular tissue revealed that E_SD was increased in the early follicular phase compared with the luteal phase. Means ± SD of E_min, E_max and E_mean in glandular tissue were 5.174 ± 2.138, 8.308 ± 3.166 and 6.593 ± 2.510, respectively, and in adipose tissue, 3.589 ± 2.083, 6.733 ± 3.522 and 4.857 ± 2.564, respectively. There were no significant differences in stiffness between glandular and adipose tissues throughout the menstrual cycle, but glandular tissue stiffness was lower in the luteal phase than in the early follicular phase. On the basis of these observations in normal healthy women, we believe we have obtained sufficient information to establish the baseline changes in human breast elasticity during the menstrual cycle. In the future, we intend to compare the elasticity values of healthy breast tissue with those of breast tissue affected by various pathologies. Our results reveal the significant potential of RT-SWE in the rapid and non-invasive clinical diagnosis of breast diseases, such as breast cancers.     

自动乳腺超声诊断系统结合计算机辅助检测乳腺恶性肿瘤敏感度的影响因素分析

目的评估影响计算机辅助检测（CAD）识别自动乳腺超声诊断系统（ABUS）乳腺恶性肿瘤敏感度的因素。 方法收集自2016年1月至2017年2月于空军军医大学西京医院行ABUS检查并经外科手术或组织学活检病理证实的乳腺恶性肿瘤患者232例，共240个恶性病灶。所有病例均经CAD软件检测，统计CAD对病灶的总敏感度，并统计分析病灶组织学类型、最大径、距乳头距离、距皮肤距离及象限等因素与CAD敏感度之间的关系。以外科手术或组织学活检病理结果为诊断"金标准"，采用χ²检验分析病灶组织学类型、最大径、距乳头距离、距皮肤距离、象限、病灶边缘特征等因素与CAD敏感度的关系。 结果CAD对恶性病灶的总敏感度为85%（204/240），对不同病理学类型的敏感度分别为：浸润性导管癌89.0%（186/209）、导管原位癌53.9%（14/29）、黏液癌75.0%（3/4）、恶性叶状肿瘤100%（1/1），差异有统计学意义（χ²=18.836，P<0.001）。病灶最大径、距乳头距离、距皮肤距离及象限均与CAD敏感度之间比较，差异无统计学意义（P>0.05）。病灶距皮肤距离、病灶边缘特征与CAD对浸润性导管癌的敏感度之间比较，差异有统计学意义（P<0.05）。 结论CAD对恶性病灶的敏感度较高（85.0%），尤其是对浸润性导管癌的检出（89.0%），医师在借助CAD读图时，应注意是否有遗漏的导管原位癌、位置深或边缘模糊的浸润性导管癌。