Improved assessment of middle ear recurrent/residual cholesteatomas using temporal subtraction CT

Purpose The purpose of this study was to investigate the usefulness of temporal subtraction CT (TSCT) of temporal bone CT for the detection of postoperative recurrent/residual cholesteatoma of the middle ear. Methods Thirty-two consecutive patients with surgically proven postoperative recurrent/residual cholesteatoma and 14 consecutive patients without recurrent/residual lesion matched the selection criteria and were retrospectively evaluated. TSCT imaging was generated with the use of serial postoperative CT. Two experienced radiologists and two residents evaluated the presence of bone erosive change by comparison serial CT studies, and CT and TSCT. The detection rate of bone erosive change, sensitivity and specificity of the recurrence/residual lesions, and reading time for each reader were evaluated. Results TSCT + CT significantly improved the detection of bone erosive changes compared to CT-only evaluation (17.4–41.3% vs. 37.0–58.7%, p = 0.008–0.046). The mean sensitivity and specificity of TSCT + CT for experienced radiologists were 0.77 and 1.00, and 0.52 and 0.97 without TSCT. The mean sensitivity and specificity of TSCT + CT for residents were 0.64 and 1.00, and 0.41 and 1.00 without TSCT. Sensitivity showed an increase in all readers. The use of TSCT significantly reduced the reading time per case in all readers (p < 0.001). Conclusion TSCT improves the depiction of newly occurring progressive bone erosive changes, and detection sensitivity and reading time in postoperative recurrence/residual cholesteatoma of middle ear.

Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study

To determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P < 0.001). At the subject-based, mean sensitivity was significantly higher for interpretation with TS images than that without TS images (73.2% vs. 65.4%; P = 0.003). There was no significant difference in mean specificity (0.93 vs. 0.95; P = 0.083). TS significantly improved overall performance in the detection of various bone metastases.

Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications

Background Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. Methods One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. Results Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). Conclusion Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems.