METHODS: The 4-h our virtual meeting in October 2020 brought together 26 experts from 14 APAC countries to discuss APCCC 2019 recommendations. Presentations were prerecorded and viewed prior to the meeting. A postmeeting survey gathered views on current practice.
RESULTS: The meeting and survey highlighted several developments since APAC APCCC 2018. Increased access and use in the region of PSMA PET/CT imaging is providing additional diagnostic and staging information for advanced prostate cancer and influencing local and systemic therapy choices. Awareness of oligometastatic disease, although not clearly defined, is increasing. Novel androgen receptor pathway antagonists are expanding treatment options. Cost and access to contemporary treatments and technologies continue to be a significant factor influencing therapeutic decisions in the region. With treatment options increasing, multidisciplinary treatment planning, shared decision making, and informed choice remain critical. A discussion on the COVID-19 pandemic highlighted challenges for diagnosis, treatment, and clinical trials and new service delivery models that will continue beyond the pandemic.
CONCLUSION: APAC-specific prostate cancer research and data are important to ensure that treatment guidelines and recommendations reflect local populations and resources. Facilitated approaches to collaboration across the region such as that achieved through APAC APCCC meetings continue to be a valuable mechanism to ensure the relevance of consensus guidelines within the region.
METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.
RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.
CONCLUSION: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.
METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.
CONCLUSION: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.
OBJECTIVE: To evaluate the initial staging discrepancy between conventional contrasted computed tomography (CT) and 18F-fluorodeoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) and its impact on management plans for head and neck malignancies.
DESIGN AND SETTING: Prospective cross-sectional study in two tertiary-level hospitals.
METHODS: This study included 30 patients with primary head and neck malignant tumors who underwent contrasted computed tomography and whole-body 18F-FDG PET/CT assessments. The staging and treatment plans were compared with the incremental information obtained after 18F-FDG PET/CT.
RESULTS: 18F-FDG PET/CT was found to raise the stage in 33.3% of the cases and the treatment intent was altered in 43.3% of them, while there was no management change in the remaining 56.7%. 18F-FDG PET/CT had higher sensitivity (96% versus 89.2%) and accuracy (93% versus 86.7%) than conventional contrast-enhanced computed tomography.
CONCLUSION: Our study demonstrated that 18F-FDG PET/CT had higher sensitivity and accuracy for detecting head and neck malignancy, in comparison with conventional contrast-enhanced computed tomography. 18F-FDG PET/CT improved the initial staging and substantially impacted the management strategy for head and neck malignancies.