On the meaning of the weighted alternative free-response operating characteristic figure of merit

Targeted Training Reduces Search Errors but Not Classification Errors for Hepatic Metastasis Detection at Contrast-Enhanced CT

RATIONALE AND OBJECTIVES

Methods are needed to improve the detection of hepatic metastases. Errors occur in both lesion detection (search) and decisions of benign versus malignant (classification). Our purpose was to evaluate a training program to reduce search errors and classification errors in the detection of hepatic metastases in contrast-enhanced abdominal computed tomography (CT).

MATERIALS AND METHODS

After Institutional Review Board approval, we conducted a single-group prospective pretest-posttest study. Pretest and posttest were identical and consisted of interpreting 40 contrast-enhanced abdominal CT exams containing 91 liver metastases under eye tracking. Between pretest and posttest, readers completed search training with eye-tracker feedback and coaching to increase interpretation time, use liver windows, and use coronal reformations. They also completed classification training with part-task practice, rating lesions as benign or malignant. The primary outcome was metastases missed due to search errors (<2 seconds gaze under eye tracker) and classification errors (>2 seconds). Jackknife free-response receiver operator characteristic (JAFROC) analysis was also conducted.

RESULTS

A total of 31 radiologist readers (8 abdominal subspecialists, 8 nonabdominal subspecialists, 15 senior residents/fellows) participated. Search errors were reduced (pretest 11%, posttest 8%, difference 3% [95% confidence interval, 0.3%-5.1%], P = .01), but there was no difference in classification errors (difference 0%, P = .97) or in JAFROC figure of merit (difference -0.01, P = .36). In subgroup analysis, abdominal subspecialists demonstrated no evidence of change.

CONCLUSION

Targeted training reduced search errors but not classification errors for the detection of hepatic metastases at contrast-enhanced abdominal CT. Improvements were not seen in all subgroups.

Periapical Radiography versus Cone Beam Computed Tomography in Endodontic Disease Detection: A Free-response, Factorial Study

AIM

To assess and compare reader performance in interpreting digital periapical (PA) radiography and cone beam computed tomography (CBCT) in endodontic disease detection, using a free-response, factorial model.

MATERIALS AND METHODS

A reader performance study of 2 image test sets was undertaken using a factorial, free-response design, accounting for the independent variables: case type, case severity, reader type, and imaging modality. Twenty-two readers interpreted 60 PA and 60 CBCT images divided into 5 categories: diseased-subtle, diseased-moderate, diseased-obvious, nondiseased-subtle, and nondiseased-obvious. Lesion localization fraction, specificity, false positive (FP) marks, and the weighted alternative free-response receiver operating characteristic figure of merit were calculated.

RESULTS

CBCT had greater specificity than PA in the obvious nondiseased cases (P = .01) and no significant difference in the subtle nondiseased category. Weighted alternative free-response receiver operating characteristic values were higher for PA than CBCT in the subtle diseased (P = .02) and moderate diseased (P = .01) groups with no significant difference between in the obvious diseased groups. CBCT had higher mean FPs than PA (P < .05) in subtle diseased cases. Mean lesion localization fraction in the moderate diseased group was higher in PA than CBCT (P = .003). No relationships were found between clinical experience and all diagnostic performance measures, except for in the obvious diseased CBCT group, where increasing experience was associated mean FP marks (P = .04).

CONCLUSIONS

Reader performance in the detection of endodontic disease is better with PA radiography than CBCT. Clinical experience does not impact upon the accuracy of interpretation of both PA radiography and CBCT.

The effect of clinical history on diagnostic performance of endodontic cone-beam CT interpretation

AIM

To assess the effect of clinical history on the interpretation of endodontic disease in dental cone-beam computed tomography (CBCT).

MATERIALS AND METHODS

A reader performance study of an image test-set was undertaken using a factorial, free-response, crossover design, accounting for the independent variables: case type, case severity, reader type, and reading modality. Twenty-three readers interpreted 60 CBCT images twice over two reading sessions using a balanced design, once with access to clinical history and once without, where 30 in each session included history. Lesion localisations, specificity, false-positive marks and the weighted alternative free-response receiver operating characteristic (wAFROC1) figure of merit were calculated.

RESULTS

Clinical history had no significant effect on specificity and false-positive rates in non-diseased cases (p>0.05), but improved lesion localisation in subtle and obvious diseased cases (p<0.01). wAFROC1 values were higher with clinical history for subtle (0.58 versus 0.48; p<0.001) and obvious (0.77 versus 0.71; p=0.006) diseased categories. No associations were observed between clinical history and both readers' years of experience and reading volume in the non-diseased categories. Readers with fewer (p=0.03) and moderate (p=0.008) years of experience and low (p=0.002) CBCT reading volume demonstrated better lesion localisation in subtle diseased cases when clinical history was available.

CONCLUSIONS

Clinical history improved the interpretation of CBCT images with disease without affecting the interpretation of images without disease. Less and moderately experienced readers and low-volume readers benefitted more from availability of clinical history.

Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT

OBJECTIVES

To develop and evaluate a deep learning-based algorithm (DLA) for automatic detection of bone metastases on CT.

METHODS

This retrospective study included CT scans acquired at a single institution between 2009 and 2019. Positive scans with bone metastases and negative scans without bone metastasis were collected to train the DLA. Another 50 positive and 50 negative scans were collected separately from the training dataset and were divided into validation and test datasets at a 2:3 ratio. The clinical efficacy of the DLA was evaluated in an observer study with board-certified radiologists. Jackknife alternative free-response receiver operating characteristic analysis was used to evaluate observer performance.

RESULTS

A total of 269 positive scans including 1375 bone metastases and 463 negative scans were collected for the training dataset. The number of lesions identified in the validation and test datasets was 49 and 75, respectively. The DLA achieved a sensitivity of 89.8% (44 of 49) with 0.775 false positives per case for the validation dataset and 82.7% (62 of 75) with 0.617 false positives per case for the test dataset. With the DLA, the overall performance of nine radiologists with reference to the weighted alternative free-response receiver operating characteristic figure of merit improved from 0.746 to 0.899 (p < .001). Furthermore, the mean interpretation time per case decreased from 168 to 85 s (p = .004).

CONCLUSION

With the aid of the algorithm, the overall performance of radiologists in bone metastases detection improved, and the interpretation time decreased at the same time.

KEY POINTS

• A deep learning-based algorithm for automatic detection of bone metastases on CT was developed. • In the observer study, overall performance of radiologists in bone metastases detection improved significantly with the aid of the algorithm. • Radiologists' interpretation time decreased at the same time.

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.