Breast cancer is the most common cancer worldwide in females around world [11].
Breast ultrasound is a noninvasive, relatively available imaging modality that is used as complementary to mammography in screening especially in dense breasts, differentiation of solid and cystic lesions, characterization of lesions, and diagnosis of intraductal mass as well as problem-solving for mammographically detected asymmetries [12, 13].
US strain elastography is a semiquantitative imaging study that offers the ability to visualize the strain produced from applying pressure to the breast tissue. The strain is smaller in harder tissue than in softer tissue [6].
Conventional elastography software is still not available in some new ultrasound machines. Advanced intelligent elastography software can provide a solution to overcome the unavailability of elastography in ultrasound machines [7, 8].
This study aimed to compare the results of conventional ultrasound elastography and the results of advanced intelligent elastography software as well as to assess the feasibility of using the advanced intelligent elastography software to overcome the unavailability of the conventional elastography software in some new ultrasound machines.
The current study included 53 patients. Upon correlation with final diagnosis either by pathological analysis of tissues samples (for 35 lesions) or close follow-up (applied for 2 years for 18 lesions assigned BIRADS 2), twenty-five out of fifty-three lesions were benign (47.2%), and twenty-eight out of fifty-three (52.8%) lesions were malignant.
We assessed the sensitivity and specificity of the conventional US compared to the pathological results and/or close follow-up.
In the current study, we reported a sensitivity of about 60% and a specificity of about 82.14%.
In our study, we calculated conventional SR for each lesion (Figs. 2,3). According to the histopathology results or close follow-up, 13 lesions were true positives, 12 were false positives, 15 lesions were false negatives, and 13 were true negatives. Conventional SR had a sensitivity of 46.43%, a specificity of 52%, a positive predictive value of 52%, and a negative predictive value of 46.43%.
There are several factors which may affect the sensitivity and the specificity for example, the pathological distribution as well as the size and the depth of the lesions.
All the false-negative lesions (15 lesions) had SR below our cut-off point (2.4). They included one invasive tubular carcinoma grade I, one malignant papillary lesion, two mucinous carcinomas (Fig. 4), and eleven IDC grade III.
As all the sonoelastographic findings were correlated with the pathological results; this explained the number of false-positive and false-negative lesions, where we found that invasive duct carcinomas of low and intermediate grades are usually hard owing to the associated desmoplastic reaction, while the high-grade carcinomas are usually less stiff as they may undergo necrosis with fat growth along with less desmoplastic reaction. Also we observed that papillary and mucinous carcinomas are usually soft lesions on elastography as they show cystic components or mucin on histology. And so this explained the number of false negative lesions we had in our study.
Meanwhile, all the false-positive lesions (12 lesions) had SR higher than our cut-off point. They included (3) fat necrosis lesions as confirmed by the result of core biopsy, (1) chronic abscess as confirmed by the result of core biopsy, (1) lesion was diagnosed as lipoma by the ultrasound features and on follow-up, and (7) lesions were fibroadenoma.
Similarly on correlation of the sonoelastographic findings with the pathological diagnoses, we found that although most fibroadenomata shows soft character, some of them are stiff due to hyaline degeneration or calcification or being large in size and this applies on the (7) lesions of fibroadenomata postulated in our study.
We found that large size of lesions may lead to false-positive or false-negative results. Also deeply seated soft lesions may give false high results as they are compressed against the chest wall/ribs.
We assessed the advanced intelligent elastography software SR for each lesion (Figs. 2, 3). According to the histopathology results or close follow-up, 17 lesions were true-positives, 9 were false-positives, 11 lesions were false-negatives, and 16 were true-negatives. The calculated advanced intelligent elastography software strain ratio sensitivity, specificity, positive likelihood, negative likelihood, positive predictive value, and negative predictive value were 60.71%, 64%, 1.6, 0.6, 65.4%, and 59.3%, respectively.
All the false-negative lesions (11 lesions) had SR below our cut-off point (2.28). They included one invasive tubular carcinoma grade I, one malignant papillary, one lesion mucinous carcinoma, eight IDC grade III.
All the false-positive lesions (9 lesions) had SR higher than our cut-off point. They included two fat necrosis lesions as confirmed by the result of core biopsy, one chronic abscess as confirmed by the result of core biopsy, and six lesions were fibroadenoma.
In general, the histopathology/elastography results discordance owed to the same previously mentioned reasons in addition to that this novel software which needs special training.
In the current study, the strain ratio of malignant lesions was higher than that of benign ones, but the overlap is still existing so false-negative and false-positive results were unavoidable.
One of the important findings was that there was a moderate correlation between the SR calculated by the conventional elastography software (GE) and AI-elastography software. In addition, AI-elasto SR showed better results than conventional SR. The specificity and positive predictive values are better which means it could exclude negative cases better. The sensitivity and negative predictive value are better which means it could confirm positive cases better. However, the test alone could not replace the pathology.