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Objective To explore reasons for variations in anxiety in women testing positive for human papillomavirus (HPV) with normal cytology at routine HPV primary cervical cancer screening. Methods In‐depth interviews were conducted with 30 women who had tested HPV‐positive with normal cytology, including 15 with low‐to‐normal anxiety and 15 with high anxiety. Data were analysed using Framework Analysis to compare themes between low and high anxiety groups. Results Several HPV‐related themes were shared across anxiety groups, but only highly anxious women expressed fear and worry, fatalistic cognitions about cancer, fertility‐related cognitions, adverse physiological responses and changes in health behaviour(s). In comparison to those with low anxiety, women with high anxiety more strongly voiced cognitions about the 12‐month wait for follow‐up screening, relationship infidelity, a lower internal locus of control and HPV‐related symptom attributions. Conclusions Receiving an HPV‐positive with normal cytology result related to various emotional, cognitive, behavioural and physiological responses; some of which were specific to, or more pronounced in, women with high anxiety. If our observations are confirmed in hypothesis‐driven quantitative studies, the identification of distinct themes relevant to women experiencing high anxiety can inform targeted patient communications and HPV primary screening implementation policy., Highlights To our knowledge, this is the first qualitative study to explore anxiety in women testing human papillomavirus (HPV)‐positive with normal cytology at routine HPV primary screening.Our comparative qualitative design allowed thematic nuances to emerge between women who had experienced low versus high anxiety following their result.Only highly anxious women expressed fear and worry, fatalistic cognitions about cancer, fertility‐related cognitions, adverse physiological responses and changes in health behaviour(s).Highly anxious women also more strongly voiced cognitions about the 12‐month wait for follow‐up screen, reltionship infidelity, a lower internal locus of control and HPV‐related symptom attributions.These distinct themes can be used to minimise unnecessary anxiety through the development of evidence‐based patient communications at HPV primary screening.

Objective Despite efforts to reduce cancer disparities, Black women remain underrepresented in cancer research. Virtual health assistants (VHAs) are one promising digital technology for communicating health messages and promoting health behaviors to diverse populations. This study describes participant responses to a VHA‐delivered intervention promoting colorectal cancer (CRC) screening with a home‐stool test. Methods We recruited 53 non‐Hispanic Black women 50 to 73 years old to participate in focus groups and think‐aloud interviews and test a web‐based intervention delivered by a race‐ and gender‐concordant VHA. A user‐centered design approach prioritized modifications to three successive versions of the intervention based on participants' comments. Results Participants identified 26 cues relating to components of the VHA's credibility, including trustworthiness, expertise, and authority. Comments on early versions revealed preferences for communicating with a human doctor and negative critiques of the VHA's appearance and movements. Modifications to specific cues improved the user experience, and participants expressed increased willingness to engage with later versions of the VHA and the screening messages it delivered. Informed by the Modality, Agency, Interactivity, Navigability Model, we present a framework for developing credible VHA‐delivered cancer screening messages. Conclusions VHAs provide a systematic way to deliver health information. A culturally sensitive intervention designed for credibility promoted user interest in engaging with guideline‐concordant CRC screening messages. We present strategies for effectively using cues to engage audiences with health messages, which can be applied to future research in varying contexts.

Objective This study aimed to assess psychological functioning, quality of life, and regret about screening after a positive fecal immunochemical test (FIT) and subsequent colonoscopy, and to evaluate changes over time. Methods This is a prospective cohort study. Individuals aged 55 to 75 with a positive FIT that were referred for colonoscopy between July 2017 and November 2018, were invited to complete questionnaires related to psychological distress and health‐related quality of life at three predefined time points: before colonoscopy, after histopathology result notification, and after 6 months. Four questionnaires were used: the Psychological Consequences Questionnaire (PCQ), the six‐item Cancer Worry Scale (CWS), the Decision Regret Scale (DRS), and the 36‐item Short‐Form (SF‐36). Results A total of 1066 participants out of 2151 eligible individuals were included. Patients with cancer showed a significant increase in psychological dysfunction (P = .01) and cancer worry (P = .008) after colonoscopy result notification, and a decline to pre‐colonoscopy measurements after 6 months. In the no‐cancer groups, psychological dysfunction and cancer worry significantly decreased over time (P 25). A good global quality of life was reported in participants with no cancer. Conclusion Some psychological distress remains up to 6 months after colonoscopy in participants who tested false‐positive in the Dutch bowel cancer screening program.

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. Aim: To reduce the class bias caused by data imbalance. Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of “premalignancy” class is also increased, which is ideal for screening applications. Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Objective The incidence of lung cancer is four times higher in people with chronic obstructive pulmonary disease (COPD) compared with the general population. Promotion of a shorter time from symptom onset to presentation is one potential strategy for earlier lung cancer diagnosis, but distinguishing respiratory symptoms can be difficult. We investigated how the experience of COPD influences symptom appraisal and help seeking for potential lung cancer symptoms. Methods We conducted qualitative interviews with men (n = 17) and women (n = 23) aged 40 to 83 years with COPD. Topic guides drew on the integrated symptom‐response framework and covered symptom experience, interpretation, action, recognition, help seeking, evaluation, and reevaluation. We used the framework method to analyse the data. Results Participants said that they attributed chest symptoms to their COPD; no other cause was considered. Participants said that family/friends noticed changes in their symptoms and encouraged help seeking. Others felt isolated by their COPD because they could not get out, were fatigued, or were embarrassed. Participants visited health professionals frequently, but increased risk of lung cancer was not discussed. Conclusions Our study provides insight into different levels of influence on symptom appraisal and targets for intervention. Greater awareness of increased lung cancer risk and support to act on symptom changes is essential and could be achieved through a concerted information campaign. Health professionals working with people with COPD could also optimise appointments to support symptom appraisal of potential lung cancer symptoms.

Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Oral cancer is one of the most common cancers worldwide, with high mortality rates. According to the International Agency for Research on Cancer, there were an estimated 377,000 new cases of lip and oral cavity cancers in 2020, with nearly 177,000 deaths worldwide.1 Despite advances in oncology therapy, mortality rates for oral cancer remain high over the past few decades. A majority of oral cancer patients do not have access to timely, quality diagnosis, and treatment, especially in rural areas, resulting in poor survival rates. The overall 5-year survival rate of diagnosed oral cancer patients is around 50% and has varied by race and area,2 whereas survival rates as high as 65% have been reported in developed countries; in some rural areas, they can be as low as 15% depending on the affected part of the oral cavity. The 2020 Cancer Statistics Report from India estimated that 66.6% of patients suffering from head and neck cancer were diagnosed at the locally advanced stage.3 In short, patient survival rates and prognosis are severely compromised when oral cancer patients are diagnosed at more advanced stages4,5 so that enhancing early diagnosis could mean a significant rise in positive survival outcomes.6,7 As oral squamous cell carcinoma (OSCC) accounts for ∼90% of all oral cancer,8,9 these two terms tend to be used interchangeably. OSCC, which originates as an epithelial dysplasia (from a histopathologic perspective), generally develops from precursor lesions termed as oral potentially malignant disorders (OPMDs).10,11 Nevertheless, it is not inevitable that all OPMDs, even the most commonly encountered such as oral lichen planus, leukoplakia, and erythroplakia,11 result in the subsequent development of malignancies.10,12 Diagnosing OPMDs as definable diseases is also challenging due to the numerous varieties, various forms, and overlapping features. However, studies10,13,14 have found that when an OPMD changes to a nonhomogeneous presentation, it is more likely to be considered as an adverse progression, in other words, nonhomogeneous lesions have a greater risk of malignant transformation as against homogeneous lesions. Hence, compared with defined diagnosis, distinguishing the potential malignant characteristics of OPMD is a greater concern. In summary, an ideal clinical prediction method should be employed to diagnose OSCC early and in particular to assess malignancy at the OPMD stage. Currently, conventional oral examination (COE) consisting of visual and tactile assessment (followed by tissue biopsy if there are any suspicious findings) is the most routine procedure in the management of oral cancer and precursor disease. However, one limitation for COE is that several features of oral cancer may appear benign and even mimic aphthous ulcers, and they are too clinically heterogeneous and subtle for general dentists to distinguish. Second, because of its invasive nature and sampling bias that can lead to underdiagnosis or misdiagnosis, biopsy is often not ideal as a screening tool.15 Furthermore, although specialists can recognize most of the characteristics that differentiate benign and cancerous lesions, the number of specialists and health resources are limited and concentrated across regions, causing a large part of the oral cancer burden to fall on low-resource communities. Therefore, the idea of establishing a cost-effective screening strategy as an adjunctive aid to the current procedures is gaining widespread popularity.16 Recently, deep learning techniques have exhibited a comparative advantage over feature-based methods in medical image analysis. A variety of studies17,18 showed that deep learning algorithms are able to surpass the performance of human experts in many disease recognition scenarios. In oral cancer diagnosis, deep learning methods also showed promising results for automatic analysis of pathology, confocal laser endomicroscopy (CLE) images, and fluorescence images. For example, Kumar et al.19 proposed a two-stage method that used a segmentation network and a random forest tree classifier to identify different stages of oral cancer in histological images. Aubreville et al.20 tested the deep convolutional neural network for OSCC diagnosis on CLE images, and the results showed that it outperformed the feature-based classification methods. Song et al.21,22 developed mobile connected devices to acquire fluorescence oral images and used them to identify oral disease. However, these methods all require expensive devices or a specifically designed screening platform, which are not accessible to everyone. In other words, patients are still required to go a professional clinic to receive disease diagnosis. With the rapid development of both imaging and sensing technologies in camera systems, the ubiquity of smartphones is equipped with higher quality, low-noise, and faster camera modules. Smartphone-based white light inspection methods23–25 are good solutions for acquiring oral images. Camalan et al.23 used a CNN-based network for classifying white light images as normal or suspicious; however, the patient sample is very limited: with only 54 cases. To build a reliable system, Welikala et al.24 collected more clinically labeled data for network training and evaluation, achieving a sensitivity of 52.13% and a specificity of 49.11% on multiclass classification task. However, we found that the clinical imaging capturing from hand-held smartphone cameras may exhibit a large variability, leading to poor diagnostic performance of the detection algorithm. For example, from a computer vision point of view, the shape and size of imaging lesions depend on the fields-of-view and focal distance, respectively. Unfortunately, a limited amount of research focuses on this problem. To address these challenges, we aim to explore reliable and robust smartphone-based white light image approaches, including an image-capturing method, resampling method, and high performance of CNN model, for oral disease recognition. Our main contributions are summarized as follows. 1. We propose a simple yet effective image-capturing method for consistent lesion position and focal distance over different images. The method allows direct focus on discriminative parts for disease recognition, without utilizing any region proposal methods,24,25 or relying on bounding box annotation24 by oral specialists. 2. We present a resampling method to alleviate the effect of image variability that introduced by the hand-held smartphone camera; at the same time, it can remedy the class imbalance problem. 3. We use one of the latest proposed convolutional neural networks (HRNet) for oral disease classification and achieve better results than common classification models. Analysis of the model’s performance on our collected images shows that common imaging pattern on a smartphone is a valuable approach for the early diagnosis. 2. Materials and Methods 2.1. Image-Capturing Method The focal length of the main camera in a smartphone is commonly short, for example, the iPhone 12 has a wide-angle (only 26 mm) camera. The focal length number tells us how much of the scene is captured in the picture, and the lower the number is, the wider the view is. Since most lesions are relatively small, we cannot capture the photograph where the lesion occupies most areas of the image. This means that the captured image may have many irrelevant backgrounds. In addition, even with the same lesion, the size of the imaging lesion may vary with different distances between the camera and lesion or using different cameras with different focal lengths. Thus these would introduce the large variability to the system’s performance going forward, and we need to find a method to reduce this considerable variability. Although identifying oral disease is very difficult for the nonmedical expert, locating the position of oral lesions is relatively easy because the visual appearance of normal and diseased tissue is significantly different. We use the camera grid to assist in locating the lesion in the center of image and to keep each area of lesion in the images neither too small nor too big. Thus it is possible to use the fixed region of interest (ROI) method to crop the discriminative parts and filter the irrelevant backgrounds, without utilizing any region proposal methods or relying on any manually cropped methods. This particular positioning of the main object in an image is helpful to improve the performance of CNN for image recognition. We use the native phone’s camera app to capture the oral cavity image. As shown in Fig. 1, the camera grid helps us to see if the lesion is properly placed at the center for optimal balance in the shot. The operation can be easily done by a person using a hand-held smartphone camera. The basic steps are as follows. Open in a separate window Fig. 1 Illustration of data acquisition. The aspect ratio is set 4:3; the image shows a lesion at the center of the region.

ObjectiveIncreased knowledge of breast cancer risk factors enables a shift from one‐size‐fits‐all breast cancer screening to a risk‐based approach, tailoring screening policy to a woman's individual risk. New opportunities for prevention will arise. However, before this novel screening and prevention program is introduced, its acceptability from a woman's perspective needs to be explored.MethodsWomen eligible for breast cancer screening in the Netherlands, United Kingdom, and Sweden were invited to take part in focus groups. A total of 143 women participated. Data were transcribed verbatim and analyzed using thematic analysis.ResultsAnalysis identified five themes across the three countries. The first theme “impact of knowledge” describes women's concern of not being able to unlearn their risk, perceiving it as either a motivator for change or a burden which may lead to stigma. The second theme “belief in science” explains women's need to trust the science behind the risk assessment and subsequent care pathways. Theme three “emotional impact” explores, eg, women's perceived anxiety and (false) reassurance, which may result from knowing their risk. Theme four “decision making” highlights cultural differences in shared versus individual decision making. Theme five “attitude to medication” explores the controversial topic of offering preventative medication for breast cancer risk reduction.ConclusionsAcceptability of risk‐based screening and prevention is mixed. Women's perceptions are informed by a lack of knowledge, cultural norms, and common emotional concerns, which highlights the importance of tailored educational materials and risk counselling to aid either shared or individual informed decision making.

Significance: Mueller matrix polarimetry can provide useful information about the function and structure of the extracellular matrix. A portable and low-cost system could facilitate the clinical assessment of cervical anomalies in low-resource settings. Aim: We introduce a low-cost snapshot Mueller matrix polarimeter that does not require external power, has no moving parts, and can acquire a full Mueller matrix in ∼1 s, to conduct a feasibility study for cervical imaging in the low-resource setting. Approach: A snapshot system based on two sets of Savart plates, a ring illuminator with polarizing elements (generating four polarization states), and one camera is introduced. Stokes vectors are formulated to recover the polarization properties of the sample. Then, using Mueller matrix decomposition, the depolarization and retardance information is extracted. Results: We report the results on 16 healthy individuals (out of 22 patients imaged), whose Pap smear showed no malignant findings from mobile clinics in rural region of Mysore, India. The depolarization and retardance information was in agreement with previous reports. Conclusions: We introduce an imaging system and conducted a feasibility study on healthy individuals. This work could futurely translate into diagnostic applications to provide a quantitative platform in the clinical environment (e.g., cervical cancer screening).

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.

Ovarian cancer is the deadliest gynecologic cancer due predominantly to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Multiphoton microscopy (MPM) is a relatively new imaging technique sensitive to endogenous fluorophores, which has tremendous potential for clinical diagnosis, though it is limited in its application to the ovaries. Wide-field fluorescence imaging (WFI) has been proposed as a complementary technique to MPM, as it offers high-resolution imagery of the entire organ and can be tailored to target specific biomarkers that are not captured by MPM imaging. We applied texture analysis to MPM images of a mouse model of ovarian cancer. We also conducted WFI targeting the folate receptor and matrix metalloproteinases. We find that texture analysis of MPM images of the ovary can differentiate between genotypes, which is a proxy for disease, with high statistical significance (p

Breast cancer is one of the leading causes for cancer related deaths in women, and early detection is extremely important to improve survival rates. Currently, x-ray mammogram is the only modality for mass screening of asymptomatic women. However, it has decreased sensitivity in radiographically dense breasts, which is also associated with a higher risk for breast cancer. Photoacoustic (PA) imaging is an emerging modality that enables deep tissue imaging of optical contrast at ultrasonically defined spatial resolution, which is much higher than that can be achieved in purely optical imaging modalities. Because of high optical absorption from hemoglobin molecules, PA imaging can map out hemo distribution and dynamics in breast tissue and identify malignant lesions based on tumor associated angiogenesis and hypoxia. We review various PA breast imaging systems proposed over the past few years and summarize the PA features of breast cancer identified in these systems.

A remarkable progress in the development of portable paper-based biosensors (PBBs) and microfluidic paper-based analytical devices (μPADs) has recently been achieved. In these devices, a paper formed of microfibers of cellulose, a carbohydrate biopolymer, offers both an ample space in its micropores for analytical reagents storage and a capillary force to drive liquid samples to a dedicated reaction zone for instantaneous detection of the desired analytes. Owing to the low cost and ultra-high sensitivity, these novel devices have become a promising alternative to traditional advanced analytical instruments and offer great potential for applications in medical emergencies, health diagnostics at points-of-care, and broad early-cancer screening. In this review, we focus particularly on recent important achievements in utilization of cellulose and its modifications in portable sensing devices for biomedical applications. The progress in functionalization of cellulose papers with antibodies, nucleic acids and nanomaterials in PBBs and μPADs, is discussed and critically evaluated.

The aim of this study was to develop a paper-based immunosensor for cervical cancer screening, with signal amplification by multifunctionalized gold nanoparticles (AuNPs). The AuNPs were functionalized with a highly specific antibody to the p16

We report the ex vivo results of an in-house fabricated portable device based on polarized fluorescence measurements in the clinical environment. This device measures the polarized fluorescence and elastic scattering spectra with 405-nm laser and white light sources, respectively. The dominating fluorophore with 405-nm excitation is flavin adenine dinucleotide (FAD) with a fluorescence peak around 510 nm. The measured spectra are highly modulated by the interplay of scattering and absorption effects. Due to this, valuable information gets masked. To reduce these effects, intrinsic fluorescence was extracted by normalizing polarized fluorescence spectra with polarized elastic scattering spectra obtained. A number of fluorophores contribute to the fluorescence spectra and need to be decoupled to understand their roles in the progression of cancer. Nelder–Mead method has been utilized to fit the spectral profile with Gaussian to decouple the different bands of contributing fluorophores (FAD and porphyrin). The change in concentration of FAD during disease progression manifests in the change in ratio of total area to FWHM of its Gaussian profile. Receiver operating characteristic (ROC) curve analysis has been used to discriminate different grades of cervical precancer by using the ratio as input parameter. The sensitivity and specificity for discrimination of normal samples from CIN I (cervical intraepithelial neoplasia) are 75% and 54%, respectively. Further, the normal samples can be discriminated from CIN II samples with 100% and 82% sensitivity and specificity, respectively, and the CIN I from CIN II samples can also be discriminated with 100% sensitivity and 90% specificity, respectively. The results show that the change in the concentration of (FAD) can be used as a marker to discriminate the different grades of the cancer and biochemical changes at an early stage of the cancer can also be monitored with this technique.

Objective: Colorectal cancer (CRC) is the third most common type of cancer with resulting major mortality. In a bid to reduce the mortality, bowel cancer screening has been established in the United Kingdom. The screening programme was commenced in Northern Ireland in 2010 within the Northern Health and Social Care Trust, following its implementation in England and Scotland. This study aimed to look at early outcome data for bowel cancer screening in Northern Ireland and compare data with other regions in the UK. Design: A retrospective analysis was conducted of patients who tested faecal occult blood (FOB) positive and attended for pre-assessment between May 2010 and May 2011. Data was also collected from the computerised endoscopy database (Endoscribe®). Patient demographics, colonoscopic depth of insertion, findings and complications were documented. Subsequent surgical management, pathological staging and final outcome were also noted. Results: 182 patients attended for pre-assessment in the time frame and 178 patients proceeded to colonoscopy. The commonest pathology encountered was polyps, identified in 95 (52.7%) patients. Macroscopically 13 cancers were seen on endoscopy and a further two were found on post-operative histology of polyps that were not amenable to endoscopic resection. In addition, 5 malignant polyps were found on histological analysis of the excised polyps. The staging of cancers was favourable with 35% being Dukes’ A stage. Conclusion: Outcomes from the first year of colorectal cancer screening in the Northern Trust are in keeping with early results from previous studies in terms of cancer detection rates per colonoscopy and proportion of early stage cancers. However, the adenoma detection rate was higher than anticipated.

A novel addressable electrode array based on paper was assembled on the crossing points of the row/column electrodes to form a 4 × 6 sensor array by a facile home-made device-holder, one paper layer contained the sensing sites, the other paper layer the printed counter electrode and reference electrode.