MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.
MATERIALS AND METHODS: Two EGFR mutation tests, a tissue-based assay (cobas® v1) and a tissue- and blood-based assay (cobas® v2) were used to analyze matched biopsy and blood samples (897 paired samples) from three Asian studies of first-line erlotinib with similar intent-to-treat populations. ENSURE was a phase III comparison of erlotinib and gemcitabine/platinum, FASTACT-2 was a phase III study of gemcitabine/platinum plus erlotinib or placebo, and ASPIRATION was a single-arm phase II study of erlotinib. Agreement statistics were evaluated, based on sensitivity and specificity between the two assays in subgroups of patients with increasing tumor burden.
RESULTS: Patients with discordant EGFR (tissue+/plasma-) mutation status achieved longer progression-free and overall survival than those with concordant (tissue+/plasma+) mutation status. Tumor burden was significantly greater in patients with concordant versus discordant mutations. Pooled analyses of data from the three studies showed a sensitivity of 72.1% (95% confidence interval [CI] 67.8-76.1) and a specificity of 97.9% (95% CI 96.0-99.0) for blood-based testing; sensitivity was greatest in patients with larger baseline tumors.
CONCLUSIONS: Blood-based EGFR mutation testing demonstrated high specificity and good sensitivity, and offers a convenient and easily accessible diagnostic method to complement tissue-based tests. Patients with a discordant mutation status in plasma and tissue, had improved survival outcomes compared with those with a concordant mutation status, which may be due to their lower tumor burden. These data help to inform the clinical utility of this blood-based assay for the detection of EGFR mutations.
RESULTS: All of the mutations were found in adenocarcinoma, except one that was in squamous cell carcinoma. The mutation rate was 45.7% (221/484). Complex mutations were also observed, wherein 8 tumours carried 2 mutations and 1 tumour carried 3 mutations.
CONCLUSIONS: Both methods detected EGFR mutations in FFPE samples. HRM assays gave more EGFR positive results compared to Scorpion ARMS.