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Background: This study aimed (I) to investigate the clinical implication of computed tomography (CT) cavity volume in tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD), and (II) to develop a three-dimensional (3D) nnU-Net model to automatically detect and quantify cavity volume on CT images. Methods: We retrospectively included conveniently sampled 206 TB and 186 NTM-PD patients in a tertiary referral hospital, who underwent thin-section chest CT scans from 2012 through 2019. TB was microbiologically confirmed, and NTM-PD was diagnosed by 2007 Infectious Diseases Society of America/American Thoracic Society guideline. The reference cavities were semi-automatically segmented on CT images and a 3D nnU-Net model was built with 298 cases (240 cases for training, 20 for tuning, and 38 for internal validation). Receiver operating characteristic curves were used to evaluate the accuracy of the CT cavity volume for two clinically relevant parameters: sputum smear positivity in TB and treatment in NTM-PD. The sensitivity and false-positive rate were calculated to assess the cavity detection of nnU-Net using radiologist-detected cavities as references, and the intraclass correlation coefficient (ICC) between the reference and the U-Net-derived cavity volumes was analyzed. Results: The mean CT cavity volumes in TB and NTM-PD patients were 11.3 and 16.4 cm3, respectively, and were significantly greater in smear-positive TB (P<0.001) and NTM-PD necessitating treatment (P=0.020). The CT cavity volume provided areas under the curve of 0.701 [95% confidence interval (CI): 0.620-0.782] for TB sputum positivity and 0.834 (95% CI: 0.773-0.894) for necessity of NTM-PD treatment. The nnU-Net provided per-patient sensitivity of 100% (19/19) and per-lesion sensitivity of 83.7% (41/49) in the validation dataset, with an average of 0.47 false-positive small cavities per patient (median volume, 0.26 cm3). The mean Dice similarity coefficient between the manually segmented cavities and the U-Net-derived cavities was 78.9. The ICCs between the reference and U-Net-derived volumes were 0.991 (95% CI: 0.983-0.995) and 0.933 (95% CI: 0.897-0.957) on a per-patient and per-lesion basis, respectively. Conclusions: CT cavity volume was associated with sputum positivity in TB and necessity of treatment in NTM-PD. The 3D nnU-Net model could automatically detect and quantify mycobacterial cavities on chest CT, helping assess TB infectivity and initiate NTM-TB treatment.
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OBJECTIVES: To investigate whether 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography/magnetic resonance imaging (PET/MRI) can improve the diagnostic performance of TNM staging and help in making an accurate decision regarding resectability in patients with recurrent gastric cancer compared to multi-detector computed tomography (MDCT). METHODS: Fifty patients with histologically (n = 31) or clinically (n = 19) confirmed recurrent gastric cancer underwent both MDCT and [18F]FDG PET/MRI. Two radiologists independently assessed TNM staging using MDCT with and without [18F]FDG PET/MRI and scored resectability using a 5-point confidence scale. Diagnostic performance as assessed by radiologists was compared using McNemar's test and receiver operating characteristic curve analysis. RESULTS: Of the 50 patients, pathologic T and N staging was available in seven and six patients, respectively. Diagnostic accuracies for T and N staging were not significantly different between MDCT with and without [18F]FDG PET/MRI for both reviewers (p > 0.05). However, for M staging, diagnostic accuracy was significantly improved when 18F-FDG PET/MRI was added to MDCT alone (68.0% [34/50] to 90.0% [45/50] for reviewer 1 [p = 0.001] and 66.0% [33/50] to 96.0% [46/50] for reviewer 2 [p < 0.001]). Regarding the resectability of recurrent gastric cancers, the addition of [18F]FDG PET/MRI increased the area under the curve values for both reviewers (from 0.860 to 0.989 for reviewer 1 and from 0.778 to 0.898 for reviewer 2), with a statistical significance for reviewer 2 (p = 0.002). CONCLUSION: Compared to MDCT alone, MDCT plus [18F]FDG PET/MRI can improve the diagnostic accuracy for evaluating preoperative M staging as well as resectability for recurrent gastric cancers. KEY POINTS: ⢠[18F]FDG PET/MRI can improve diagnostic accuracy for preoperative M staging in patients with recurrent gastric cancers. ⢠[18F]FDG PET/MRI can improve diagnostic accuracy for determining resectability in patients with recurrent gastric cancers. ⢠[18F]FDG PET/MRI can provide critical clues for management options for recurrent gastric cancers.
