Dataset Information

Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.

ABSTRACT:

Background

Detecting new pulmonary metastases by comparing serial computed tomography (CT) scans is crucial, but a repetitive and time-consuming task that burdens the radiologists' workload. This study aimed to evaluate the usefulness of a nodule-matching algorithm with deep learning-based computer-aided detection (DL-CAD) in diagnosing new pulmonary metastases on cancer surveillance CT scans.

Methods

Among patients who underwent pulmonary metastasectomy between 2014 and 2018, 65 new pulmonary metastases missed by interpreting radiologists on cancer surveillance CT (Time 2) were identified after a retrospective comparison with the previous CT (Time 1). First, DL-CAD detected nodules in Time 1 and Time 2 CT images. All nodules detected at Time 2 were initially considered metastasis candidates. Second, the nodule-matching algorithm was used to assess the correlation between the nodules from the two CT scans and to classify the nodules at Time 2 as "new" or "pre-existing". Pre-existing nodules were excluded from metastasis candidates. We evaluated the performance of DL-CAD with the nodule-matching algorithm, based on its sensitivity, false-metastasis candidates per scan, and positive predictive value (PPV).

Results

A total of 475 lesions were detected by DL-CAD at Time 2. Following a radiologist review, the lesions were categorized as metastases (n=54), benign nodules (n=392), and non-nodules (n=29). Upon comparison of nodules at Time 1 and 2 using the nodule-matching algorithm, all metastases were classified as new nodules without any matching errors. Out of 421 benign lesions, 202 (48.0%) were identified as pre-existing and subsequently excluded from the pool of metastasis candidates through the nodule-matching algorithm. As a result, false-metastasis candidates per CT scan decreased by 47.9% (from 7.1 to 3.7, P<0.001) and the PPV increased from 11.4% to 19.8% (P<0.001), while maintaining sensitivity.

Conclusions

The nodule-matching algorithm improves the diagnostic performance of DL-CAD for new pulmonary metastases, by lowering the number of false-metastasis candidates without compromising sensitivity.

SUBMITTER: Yoon SH

PROVIDER: S-EPMC10895128 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.

Yoon Sung Hyun SH Oh Dong Yul DY Kim Hyo Jin HJ Jang Sowon S Kim Minseon M Kim Jihang J Lee Kyung Won KW Lee Kyong Joon KJ Kim Junghoon J

Quantitative imaging in medicine and surgery 20240102 2

<h4>Background</h4>Detecting new pulmonary metastases by comparing serial computed tomography (CT) scans is crucial, but a repetitive and time-consuming task that burdens the radiologists' workload. This study aimed to evaluate the usefulness of a nodule-matching algorithm with deep learning-based computer-aided detection (DL-CAD) in diagnosing new pulmonary metastases on cancer surveillance CT scans.<h4>Methods</h4>Among patients who underwent pulmonary metastasectomy between 2014 and 2018, 65 ...[more]

PMID: 38415154

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Dataset Information

Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.

Background

Methods

Results

Conclusions

Publications

Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.

Similar Datasets

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