DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest.

BACKGROUND: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease. OBJECTIVE: A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations. METHODS: The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing. RESULTS: The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches. CONCLUSION: The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.

Society for Immunotherapy of Cancer (SITC) recommendations on intratumoral immunotherapy clinical trials (IICT): from premalignant to metastatic disease.

BACKGROUND: Intratumorally delivered immunotherapies have the potential to favorably alter the local tumor microenvironment and may stimulate systemic host immunity, offering an alternative or adjunct to other local and systemic treatments. Despite their potential, these therapies have had limited success in late-phase trials for advanced cancer resulting in few formal approvals. The Society for Immunotherapy of Cancer (SITC) convened a panel of experts to determine how to design clinical trials with the greatest chance of demonstrating the benefits of intratumoral immunotherapy for patients with cancers across all stages of pathogenesis. METHODS: An Intratumoral Immunotherapy Clinical Trials Expert Panel composed of international key stakeholders from academia and industry was assembled. A multiple choice/free response survey was distributed to the panel, and the results of this survey were discussed during a half-day consensus meeting. Key discussion points are summarized in the following manuscript. RESULTS: The panel determined unique clinical trial designs tailored to different stages of cancer development-from premalignant to unresectable/metastatic-that can maximize the chance of capturing the effect of intratumoral immunotherapies. Design elements discussed included study type, patient stratification and exclusion criteria, indications of randomization, study arm determination, endpoints, biological sample collection, and response assessment with biomarkers and imaging. Populations to prioritize for the study of intratumoral immunotherapy, including stage, type of cancer and line of treatment, were also discussed along with common barriers to the development of these local treatments. CONCLUSIONS: The SITC Intratumoral Immunotherapy Clinical Trials Expert Panel has identified key considerations for the design and implementation of studies that have the greatest potential to capture the effect of intratumorally delivered immunotherapies. With more effective and standardized trial designs, the potential of intratumoral immunotherapy can be realized and lead to regulatory approvals that will extend the benefit of these local treatments to the patients who need them the most.