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.
CLINICAL CASE: A 56-year-old Chinese woman, an active smoker, presented with a hearing deficit, persistent tinnitus and nasal congestion. Examination and investigations revealed the presence of a mass in the nasopharynx. Tissue biopsy revealed nasopharyngeal carcinoma. However, the Epstein-Barr virus was not tested. She was counseled for chemotherapy, but refused and was subsequently lost to follow up. She presented one year later with right eye ptosis associated with progressive worsening of diplopia and blurring of vision. Examination revealed multiple (second, third, fourth and sixth) cranial nerve involvement. Systemic examination and investigations revealed cervical lymphadenopathy and liver metastasis. Repeated imaging showed that the mass had invaded the base of the skull, cavernous sinus and orbital apices. Pulse dosing of corticosteroid therapy was commenced, resulting in dramatic improvement of vision.
CONCLUSION: Optic neuropathy may be the presenting sign of NPC. Corticosteroid therapy can offer immediate visual improvement.