Lung cancer screening via low-dose computed tomography (LDCT) has been underutilized by high-risk current and former smokers since its approval in 2013. Further, lower use of other evidence-based cancer screening tests (e.g., colorectal cancer, breast cancer) has been noted among African Americans when compared with other racial and ethnic groups. Reasons for low uptake are multilayered but include the need for consideration of patients' personal values about the screening decision. The goal of the present study was to (1) identify positive and negative factors specific to lung cancer screening via LDCT and (2) develop statements to capture values about the screening test for use in a new measure of decisional values. Key informant interviews (n = 9) identified several benefits and risks of lung cancer screening that may be important to African American smokers. Based on these interviews, a pool of items with the values statements was administered to a convenience sample of 119 African Americans [aged 55-80 years, current or former smokers (who quit < 15 years), and without lung cancer]. An exploratory factor analysis revealed two components explaining 64% of the variance: cons of screening (e.g., "make you feel badly about your smoking history") and pros of screening (e.g., "lowering your risk of dying from lung cancer"). The final 12-item measure had very good internal consistency (α = 0.89 overall; α = 0.86 and 0.88 for subscales, respectively). This tool provides a promising values measure for lung cancer screening among African Americans and could inform future values clarification tools promoting informed and shared decision-making.
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http://dx.doi.org/10.1007/s13187-020-01687-4 | DOI Listing |
Int J Clin Oncol
January 2025
Translational Research Support Section, National Cancer Center Hospital East, Chiba, Japan.
Early cancer detection substantially improves the rate of patient survival; however, conventional screening methods are directed at single anatomical sites and focus primarily on a limited number of cancers, such as gastric, colorectal, lung, breast, and cervical cancer. Additionally, several cancers are inadequately screened, hindering early detection of 45.5% cases.
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January 2025
Department of Community & Family Medicine, All India Institute of Medical Sciences, 151001, Bathinda, Punjab, India.
Introduction: Existing evidence suggests a lower uptake of cervical cancer screening among Indian women. Coverage is lower in rural than urban women, but such disparities are less explored. So, the present study was conducted to explore the self-reported coverage of cervical cancer screening in urban and rural areas stratified by socio-demographic characteristics, determine the spatial patterns and identify any regional variations, ascertain the factors contributing to urban-rural disparities and those influencing the likelihood of screening among women aged 30-49 years factors residing in urban, rural, and overall Indian settings.
View Article and Find Full Text PDFDeep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) models in practical settings limits their clinical application. This study aims to review and analyze current SOTA deep learning models for lung cancer risk prediction (malignant-benign classification).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Pathology, School of Medical Sciences, Clinical Teaching Center, University of Cape Coast, Private Mail Bag, Cape Coast, Ghana.
Cervical cancer continues to disproportionately burden women in sub-Saharan Africa, and is the commonest gynecological cancer in Ghana. The Cervical Cancer Prevention and Training Centre (CCPTC), Battor, Ghana spearheaded the Ghana arm of the mPharma 10,000 Women Initiative (mTTWI) between September 2021 and October 2022. The aim of this study was to examine the outcomes of nationwide concurrent screening using high-risk human papillomavirus (hr-HPV) DNA testing and visual inspection methods, as well as factors associated with the screening outcomes.
View Article and Find Full Text PDFISA Trans
January 2025
Department of Electronics and Telecommunication, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India. Electronic address:
Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learning, is gaining a preferred trend of research in medical image processing because of their admirable performance, even with small datasets. It inspires us to develop a novel deep learning-based leukemia detection system in which an efficient and lightweight MobileNetV2 is used in conjunction with ShuffleNet to boost discrimination ability and enhance the receptive field via convolution layer succession.
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