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Social scientists have advocated for the use of participatory research methods for Global Health project design and planning. However, community-engaged approaches can be time and resource-intensive. This article proposes a feasible framework for conducting a participatory needs assessment in time-limited settings using multiple, triangulated qualitative methods. This framework is outlined through a case study: a participatory needs assessment to inform the design of an ultrasound-guided biopsy training program in Nigeria. Breast cancer is the leading cause of death for Nigerian women and most cases in Nigeria are diagnosed at an advanced stage; timely diagnosis is impeded by fractious referral pathways, costly imaging equipment, and limited access outside urban centers. The project involved participant observation, surveys, and focus groups at the African Research Group for Oncology (ARGO) in Ile-Ife, Nigeria. Through this timely research and engagement, participants spoke about diagnostic challenges, institutional power dynamics, and infrastructure considerations for program implementation.
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PURPOSE: The incidence of breast cancer is rising in Nigeria, and one major barrier to care is the lack of affordable and appropriate breast cancer diagnosis by ultrasound (US)-guided biopsy. The prohibitive cost of US devices limits their availability in low- and middle-income countries. The emergence of mobile health (mHealth) imaging devices may offer an acceptable low-cost alternative. The purpose of this research was to perform a comprehensive needs assessment to understand knowledge, use, training needs, and attitudes as regards image-guided biopsy in Nigeria to inform the development of an mHealth-based US-guided biopsy training program. METHODS: A multistakeholder needs assessment was conducted at the Sixth Annual African Research Group for Oncology Symposium. Voluntary anonymous surveys were administered to all attendees. A subset of attendees (ie, surgeons, radiologists, pathologists, and nurses) participated in six focus groups. Survey items and interview guides were developed collaboratively with local and international input. RESULTS: Surveys focusing on use, training needs, and attitudes regarding US-guided biopsies were completed with a 55% response rate (n = 54 of 98) among participants from 22 hospitals across Nigeria. Respondents expressed dissatisfaction with the way breast biopsies were currently performed at their hospitals and high interest in having their institution participate in a US-guided biopsy training program. Focus group participants (n = 37) identified challenges to performing US-guided procedures, including equipment functionality and cost, staff training, and access to consumables. Groups brainstormed the design of an mHealth US-guided biopsy training program, preferring a train-the-trainer format combining in-person teaching with independent modules. CONCLUSION: A multidisciplinary needs assessment of local stakeholders identified a need for and acceptability of an mHealth-based US-guided biopsy training program in Nigeria.
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Biópsia Guiada por Imagem , Telemedicina , Humanos , Avaliação das Necessidades , Nigéria , Ultrassonografia de IntervençãoRESUMO
PURPOSE: To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. PROCEDURES: In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. RESULTS: For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). CONCLUSIONS: Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.