Computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists

Abstract

Background

Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality.

Purpose

To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT).

Material and Methods

Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers’ sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test

Results

With CAD, less experienced radiologists’ sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% (P = 0.007), while the experienced radiologists’ sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist (P < 0.001).

Conclusion

CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.

Keywords

Computer-aided detection deep learning brain metastases computed tomography

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References

Nayak

Lee

Wen

. Epidemiology of brain metastases. Curr Oncol Rep 2012;14:48–54.

Cagney

Martin

Catalano

, et al. Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neuro Oncol 2017;19:1511–1521.

Nabors

Portnow

Ahluwalia

, et al. Central nervous system cancers, version 3.2020, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2020;18:1537–1570.

Roth

Pace

Le Rhun

, et al. Neurological and vascular complications of primary and secondary brain tumours: EANO-ESMO Clinical Practice Guidelines for prophylaxis, diagnosis, treatment and follow-up. Ann Oncol 2021;32:171–182.

Holle

Obermann

. The role of neuroimaging in the diagnosis of headache disorders. Ther Adv Neurol Disord 2013;6:369–374.

Achrol

Rennert

Anders

, et al. Brain metastases. Nat Rev Dis Primers 2019;5:5.

Pope

. Brain metastases: neuroimaging. Handb Clin Neurol 2018;149:89–112.

Whitehead

Cardenas

Corey

, et al. ACR appropriateness criteria® headache. J Am Coll Radiol 2019;16:S364–S377.

Fujita

. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first Radiol Phys Technol 2020;13:6–19.

10.

Yanase

Triantaphyllou

. A systematic survey of computer-aided diagnosis in medicine: past and present developments. Expert Syst Appl 2019;138:112821.

11.

Al Mohammad

Brennan

Mello-Thoms

. A review of lung cancer screening and the role of computer-aided detection. Clin Radiol 2017;72:433–442.

12.

Hsu

H-H

K-H

Chou

Y-C

, et al. Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system. Clin Radiol 2021;76:626.e23–626.e32.

13.

Henriksen

Carlsen

Vejborg

, et al. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol 2019;60:13–18.

14.

Zhou

Sanders

Johnson

, et al. Computer-aided detection of brain metastases in T1-weighted MRI for stereotactic radiosurgery using deep learning single-shot detectors. Radiology 2020;295:407–415.

15.

Zhang

Young

Chen

, et al. Deep-learning detection of cancer metastases to the brain on MRI. J Magn Reson Imaging 2020;52:1227–1236.

16.

Amemiya

Takao

Kato

, et al. Feature-fusion improves MRI single-shot deep learning detection of small brain metastases. J Neuroimaging 2022;32:111–119.

17.

Amemiya

Takao

Kato

, et al. Automatic detection of brain metastases on contrast-enhanced CT with deep-learning feature-fused single-shot detectors. Eur J Radiol 2021;136:109577.

18.

Sunwoo

Kim

Choi

, et al. Computer-aided detection of brain metastasis on 3D MR imaging: observer performance study. PLoS One 2017;12:e0178265.

19.

Kato

Amemiya

Takao

, et al. Automated detection of brain metastases on non-enhanced CT using single-shot detectors. Neuroradiology 2021;63:1995–2004.

20.

Boone

Mallett

McQuillan

, et al. Assessment of the incremental benefit of computer-aided detection (CAD) for interpretation of CT colonography by experienced and inexperienced readers. PLoS One 2015;10:e0136624.

21.

Kakeda

Korogi

Arimura

, et al. Diagnostic accuracy and reading time to detect intracranial aneurysms on MR angiography using a computer-aided diagnosis system. AJR Am J Roentgenol 2008;190:459–465.

22.

Freedman

Gillis

, et al. JOURNAL CLUB: computer-aided detection of lung nodules on CT with a computerized pulmonary vessel suppressed function. AJR Am J Roentgenol 2018;210:480–488.

23.

Vassallo

Traverso

Agnello

, et al. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. Eur Radiol 2019;29:144–152.

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