Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer.

PURPOSE: To explore the abnormality score trends of artificial intelligence-based computer-aided diagnosis (AI-CAD) in the serial mammography of patients until a final diagnosis of breast cancer. METHOD: From 2015 to 2019, 126 breast cancer patients who had at least two previous mammograms obtained from 2008 up to cancer diagnosis were included. AI-CAD was retrospectively applied to 487 previous mammograms and all the abnormality scores calculated by AI-CAD were obtained. The contralateral breast of each affected breast was defined as the control group. We divided all mammograms by 6-month intervals from cancer diagnosis in reverse chronological order. The random coefficient model was used to estimate whether the chronological trend of AI-CAD abnormality scores differed between cancer and normal breasts. Subgroup analyses were performed according to mammographic visibility, invasiveness and molecular subtype of the invasive cancer. RESULTS: Mean period from initial examination to cancer diagnosis was 6.0 years (range 1.7-10.7 years). The abnormality scores of breasts diagnosed with cancer showed a significantly increasing trend during the previous examination period (slope 0.6 per 6 months, p for the slope < 0.001), while the contralateral normal breast showed no trend (slope 0.03, p = 0.776). The difference in slope between the cancerous and contralateral breasts was significant (p < 0.001). For mammography-visible cancers, the abnormality scores in cancerous breasts showed a significant increasing trend (slope 0.8, p < 0.001), while for mammography-occult cancers, the trend was not significant (slope 0.1, p = 0.6). For invasive cancers, the slope of the abnormality scores showed a significant increasing trend (slope 1.4, p = 0.002), unlike ductal carcinoma in situ (DCIS) which showed no significant trend. There was no significant difference in the slope of abnormality scores among the subtypes of invasive cancers (p = 0.418). CONCLUSION: Breasts diagnosed with cancer showed an increase in AI-CAD abnormality scores in previous serial mammograms, suggesting that AI-CAD could be useful for early detection of breast cancer.

AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ.

OBJECTIVES: To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. METHODS: Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0-100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS. RESULTS: Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, p = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, p = 0.023 and OR: 5.190, p < 0.001, respectively), higher AI-CAD score (OR: 1.009, p = 0.007), AI-CAD score ≥ 50% (OR: 1.960, p = 0.017), and AI-CAD score ≥ 75% (OR: 2.306, p = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, p = 0.035), and higher AI-CAD score (OR: 1.008, p = 0.047) were significant predictors of invasive upgrade. CONCLUSION: The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of ≥ 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy. CRITICAL RELEVANCE STATEMENT: Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment. KEY POINTS: • Predicting ductal carcinoma in situ upgrade is important, yet there is a lack of conclusive non-invasive biomarkers. • AI-CAD scores-raw numbers, ≥ 50%, and ≥ 75%-predicted ductal carcinoma in situ upgrade independently. • Quantitative AI-CAD results may help predict ductal carcinoma in situ upgrade and guide patient management.

Learnability of Thyroid Nodule Assessment on Ultrasonography: Using a Big Data Set.

OBJECTIVE: The aims of the work described here were to evaluate the learnability of thyroid nodule assessment on ultrasonography (US) using a big data set of US images and to evaluate the diagnostic utilities of artificial intelligence computer-aided diagnosis (AI-CAD) used by readers with varying experience to differentiate benign and malignant thyroid nodules. METHODS: Six college freshmen independently studied the "learning set" composed of images of 13,560 thyroid nodules, and their diagnostic performance was evaluated after their daily learning sessions using the "test set" composed of images of 282 thyroid nodules. The diagnostic performance of two residents and an experienced radiologist was evaluated using the same "test set." After an initial diagnosis, all readers once again evaluated the "test set" with the assistance of AI-CAD. RESULTS: Diagnostic performance of almost all students increased after the learning program. Although the mean areas under the receiver operating characteristic curves (AUROCs) of residents and the experienced radiologist were significantly higher than those of students, the AUROCs of five of the six students did not differ significantly compared with that of the one resident. With the assistance of AI-CAD, sensitivity significantly increased in three students, specificity in one student, accuracy in four students and AUROC in four students. Diagnostic performance of the two residents and the experienced radiologist was better with the assistance of AI-CAD. CONCLUSION: A self-learning method using a big data set of US images has potential as an ancillary tool alongside traditional training methods. With the assistance of AI-CAD, the diagnostic performance of readers with varying experience in thyroid imaging could be further improved.

Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network.

To assess the performance of deep convolutional neural network (CNN) to discriminate malignant and benign thyroid nodules < 10 mm in size and compare the diagnostic performance of CNN with those of radiologists. Computer-aided diagnosis was implemented with CNN and trained using ultrasound (US) images of 13,560 nodules ≥ 10 mm in size. Between March 2016 and February 2018, US images of nodules < 10 mm were retrospectively collected at the same institution. All nodules were confirmed as malignant or benign from aspirate cytology or surgical histology. Diagnostic performances of CNN and radiologists were assessed and compared for area under curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Subgroup analyses were performed based on nodule size with a cut-off value of 5 mm. Categorization performances of CNN and radiologists were also compared. A total of 370 nodules from 362 consecutive patients were assessed. CNN showed higher negative predictive value (35.3% vs. 22.6%, P = 0.048) and AUC (0.66 vs. 0.57, P = 0.04) than radiologists. CNN also showed better categorization performance than radiologists. In the subgroup of nodules ≤ 5 mm, CNN showed higher AUC (0.63 vs. 0.51, P = 0.08) and specificity (68.2% vs. 9.1%, P < 0.001) than radiologists. Convolutional neural network trained with thyroid nodules ≥ 10 mm in size showed overall better diagnostic performance than radiologists in the diagnosis and categorization of thyroid nodules < 10 mm, especially in nodules ≤ 5 mm.

Evaluation of Sagittal Spinopelvic Alignment on Analgesic Efficacy of Lumbar Epidural Steroid Injection in Geriatric Patients.

Background and Objectives: The aim of this study was to evaluate the impact of sagittal imbalance based on pelvic incidence−lumbar lordosis (PI-LL) mismatch on the analgesic efficacy of epidural steroid injection in geriatric patients. Materials and Methods: Patients aged 65 years or older who received lumbar epidural steroid injections under fluoroscopy were enrolled. The cutoff of PI-LL mismatch >20° was used as an indicator of a marked sagittal imbalance. The cross-sectional area of the psoas and paraspinal muscles, as well as the paraspinal fat infiltration grade were measured. A 50% or more decrease in pain score at four weeks after injection was considered as good analgesia. Variables were compared between PI-LL ≤ 20° and >20° groups and multivariate analysis was used to identify factors related to pain relief after injection. Results: A total of 237 patients consisting of 150 and 87 patients in the PI-LL ≤ 20° and >20° groups, respectively, were finally analyzed. Female patients, patients with lumbar surgery history, and the smaller cross-sectional area of the psoas muscles were predominantly observed in patients with sagittal imbalance. There was no difference in analgesic outcome after injection according to the PI-LL mismatch (good analgesia 60.0 vs. 60.9%, p = 0.889). Multivariate analysis showed that pre-injection opioid use, moderate to severe foraminal stenosis, and high-graded paraspinal fat infiltration were significantly associated with poor analgesia after injection. Conclusions: There was no significant correlation between sagittal spinopelvic alignment and pain relief after lumbar epidural steroid injection for geriatric patients.

Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level.

PURPOSE: This study evaluated how artificial intelligence-based computer-assisted diagnosis (AICAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows. METHODS: Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD. RESULTS: After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists. CONCLUSION: Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.

Influence of cross-sectional area and fat infiltration of paraspinal muscles on analgesic efficacy of epidural steroid injection in elderly patients.

BACKGROUND: An assessment of paraspinal muscle degeneration based on magnetic resonance imaging has been used to investigate both sarcopenia and myosteatosis. The morphologic changes in cross-sectional area and fat infiltration of the paraspinal muscles can affect pain outcomes after epidural steroid injection. METHODS: Patients ≥65 years of age who underwent fluoroscopy-guided lumbar epidural steroid injections were enrolled. Good analgesia was defined as ≥50% reduction in pain score at 4 weeks after injection. Cross-sectional area and grade of fat infiltration of the paraspinal muscles on magnetic resonance images at the level of L3-L4 disc were measured. Patient demographics, pain-related factors, clinical factors, and paraspinal muscle measurements were compared between good and poor analgesia groups. The factors associated with pain outcomes after injection were identified using multivariate analysis. RESULTS: A total of 245 patients consisting of 149 and 96 patients in the good and poor analgesia groups, respectively, fully satisfied the study criteria for analysis. Patients of older age, opioid use, and high-grade foraminal stenosis were frequently observed in the poor analgesia group. The grade of fat infiltration of the paraspinal muscles was significantly higher in the poor analgesia group (Grade 2, 20.8% vs. 42.7%, p < 0.001), and this result was predominantly observed in female patients. However, there was no difference in the muscle cross-sectional area between the two groups (18.29 ± 3.16 vs. 18.59 ± 3.03 cm2 /m2 , p = 0.460). The percentage of patients with good analgesia decreased as the grade of fat infiltration increased (Grade 0 = 75.0%, Grade 1 = 65.8%, Grade 2 = 43.0%, p < 0.001). Multivariate logistic regression analysis revealed that preinjection opioid use [adjusted odds ratio (aOR) = 1.926, 95% confidence interval (CI) = 1.084-3.422, p = 0.025], moderate to severe foraminal stenosis (aOR = 2.859, 95% CI = 1.371-5.965, p = 0.005), and high-grade fat infiltration of the paraspinal muscles (aOR = 4.258, 95% CI = 1.805-10.043, p = 0.001) were significantly associated with poor analgesia after injection. CONCLUSION: High fat infiltration of the paraspinal muscles at the mid-lumbar region appeared to be an independent factor associated with poor analgesia after epidural steroid injection in elderly patients with symptomatic degenerative lumbar spinal disease receiving conservative care. However, the cross-sectional area of the paraspinal muscles was not associated with pain relief after injection.

Using ultrasonographic features to predict the outcomes of patients with small papillary thyroid carcinomas: a retrospective study implementing the 2015 ATA patterns and ACR TI-RADS categories.

BACKGROUND: The aim of this study was to evaluate whether risk stratification systems using ultrasonographic (US) features show associations with the outcomes of patients with small papillary thyroid carcinomas (PTCs). METHODS: This retrospective study received institutional review board approval. From March 2007 to February 2010, 775 patients who underwent surgery for small PTCs (10-20 mm) were included. Based on preoperative US features, PTCs were categorized according to the 2015 American Thyroid Association (ATA) guideline and the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). The associations of clinicopathological and US features with postoperative patient outcomes were evaluated. RESULTS: In total, 61 patients had high-volume central lymph node metastasis (CLNM, 7.9%) and 100 patients had lateral lymph node metastasis (LLNM, 12.9%). In univariable analyses, a high number of suspicious US features and higher ACR TI-RADS point totals were significantly associated with both high-volume CLNM (P=0.001, each) and LLNM (P<0.001, each). In multivariable analyses of preoperative features, a higher number of suspicious US features and higher ACR TI-RADS point totals were independently associated with high-volume CLNM (odds ratio [OR], 1.516 and 1.201; P=0.002 and P=0.001, respectively) and LLNM (OR, 1.763 and 1.293; all P<0.001). Individual US features, ATA categories, and ACR TI-RADS point totals were not significantly associated with recurrence or distant metastasis. CONCLUSION: The number of suspicious US features and the ACR TI-RADS point total are potential risk factors for cervical lymph node metastasis in patients with small PTCs.

Clinical Parameter for Deciding the BRAFV600E Mutation Test in Atypia of Undetermined Significance/Follicular Lesion of Undetermined Significance Thyroid Nodules: US Features According to TIRADS.

This study aimed to investigate the usefulness of a thyroid imaging reporting and data system (TIRADS) to select thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) cytology for additional BRAF mutation testing. Three hundred three thyroid nodules were included. Statistical analysis was performed at both patient and nodule levels according to BRAF mutation positivity and clinical factors. Univariate and multivariate logistic regression analyses were performed to assess independent associations between BRAF mutation positivity and clinical factors. Of 303 AUS/FLUS nodules, 16 (5.3%) of 303 nodules had the BRAF mutation. The frequency of the BRAF mutation according to the TIRADS was 35.7% for category 5, 10.8% for category 4c, 2.5% for category 4b, 1.1% for category 4a, and 0% for category 3 nodules (P < 0.001). On multivariate analysis, BRAF mutation positivity was significantly associated with high suspicion on the TIRADS (odds ratio, 15.247; P < 0.001). In conclusion, the ultrasonography patterns of the TIRADS can be used as a clinical parameter for deciding the BRAF mutation test in thyroid nodules with AUS/FLUS cytology.