METHODS: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.
RESULTS: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.
CONCLUSION: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.
KEY MESSAGES: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.
OBJECTIVE: This study aims to review the typical and relatively atypical CXR manifestations of COVID-19 pneumonia in a tertiary care hospital.
METHODS: The CXRs of 136 COVID-19 patients confirmed through real-time RT-PCR from March to May 2020 were reviewed. A literature search was performed using PubMed.
RESULTS: A total of 54 patients had abnormal CXR whilst the others were normal. Typical CXR findings included pulmonary consolidation or ground-glass opacities in a multifocal, bilateral peripheral, or lower zone distribution, whereas atypical CXR features comprised cavitation and pleural effusion.
CONCLUSION: Typical findings of COVID-19 infection in chest computed tomography studies can also be seen in CXR. The presence of atypical features associated with worse disease outcome. Recognition of these features on CXR will improve the accuracy and speed of diagnosing COVID-19 patients.
CASE REPORT: An elderly lady presented with an enlarging painful left breast lump for 1 year. Palpable left breast lump noted on clinical examination. Mammography demonstrated a high density, oval lesion with a partially indistinct margin. Corresponding ultrasound showed a large irregular heterogeneous lesion with solid-cystic areas. Histopathology showed atypical spindle-shaped cells which stained positive for cytokeratins and negative for hormone and human epidermal growth factor receptors, which favours spindle cell metaplastic carcinoma. Left mastectomy and axillary dissection were performed, and the final diagnosis was consistent with metaplastic spindle cell carcinoma.
CONCLUSION: Spindle cell carcinoma of the breast is a rare aggressive histological type of carcinoma which may present with benign features on imaging. Tissue diagnosis is essential for prompt diagnosis with multidisciplinary team discussion to guide management and improve patient's outcome.
CASE REPORT: A 30-year-old lady presented with left breast pain and lumpiness for over one year. She has had several breast ultrasounds (US) and was treated for acute mastitis and abscess. Subsequently, in view of the rapid growth of the lump and worsening pain, she was re-investigated with US, elastography, digital breast tomosynthesis (DBT) and MRI. MRI raised the suspicion of angiosarcoma. The diagnosis was confirmed after biopsy and she underwent mastectomy.
DISCUSSION: Literature review on imaging findings of breast angiosarcoma, especially on MRI, is discussed. MRI features showed heterogeneous low signal intensity on T1 and high signal intensity on T2. Dynamic contrast enhancement (DCE) features included either early enhancement with or without washout in the delayed phase, and some reported central areas of non-enhancement.
CONCLUSION: This case report emphasises on the importance of MRI in clinching the diagnosis of breast angiosarcoma, and hence, should be offered sooner to prevent diagnostic delay.