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Background: Safety netting in primary care may help diagnose cancer earlier, but it is unclear what the format and content of an acceptable safety-netting intervention would be. This project aimed to co-design a safety-netting intervention with and for primary care patients and staff.Aim: This work sought to address how a safety-netting intervention would be implemented in practice; and, if and how a safety-netting intervention would be acceptable to all stakeholders.Design and Setting: Patient representatives, GPs, and nurse practitioners were invited to a series of co-design workshops. Patients who had and had not received a diagnosis of cancer and primary care practices took part in separate focus groups.Method: Three workshops using creative co-design processes developed the format and content of the intervention prototype. The COM-B Framework underpinned five focus groups to establish views on capability, opportunity, and motivation to use the intervention to assist with prototype refinement.Results: Workshops and focus groups suggested the intervention format and content should incorporate visual and written communication specifying clear timelines for monitoring symptoms and when to present back; be available in paper and electronic forms linked to existing computer systems; and be able to be delivered within a 10-minute consultation. Intervention use themes included 'building confidence through partnership', 'using familiar and current procedures and systems', and 'seeing value'.Conclusion: The Shared Safety Net Action Plan (SSNAP) - a safety-netting intervention to assist the timely diagnosis of cancer in primary care, was successfully co-designed with and for patients and primary care staff. [ABSTRACT FROM AUTHOR]

Cancer is a significant global public health issue with increasing morbidity and mortality rates. To address this challenge, novel drug carriers such as nano-materials, liposomes, hydrogels, fibers, and microspheres have been extensively researched and utilized in oncology. Among them, polymer microspheres are gaining popularity due to their ease of preparation, excellent performance, biocompatibility, and drug-release capabilities. This paper categorizes commonly used materials for polymer microsphere preparation, summarizes various preparation methods (emulsification, phase separation, spray drying, electrospray, microfluidics, and membrane emulsification), and reviews the applications of polymer microspheres in cancer diagnosis, therapy, and postoperative care. The current status and future development directions of polymer microspheres in cancer treatment are analyzed, highlighting their importance and potential for improving patient outcomes. [ABSTRACT FROM AUTHOR]

Autonomy of breast cancer classification is a challenging problem, and early diagnosis is highly important. Histopathology images provide microscopic-level details of tissue samples and play a crucial role in the accurate diagnosis and classification of breast cancer. Moreover, advancements in deep learning play an essential role in early cancer diagnosis. However, existing techniques involve unique models for each classification based on the magnification factor and require training numerous models or using a hierarchical approach combining multiple models irrespective of the focus of the cell features. This may lead to lower performance for multiclass categorization. This paper adopts the DenseNet161 network by adding a learnable residual layer. The learnable residual layer enhances the features, providing low-level information. In addition, residual features are obtained from the convolution features of the preceding layer, which ensures that the future size is consistent with the number of channels in DenseNet's layer. The concatenation of spatial features with residual features helps better learn texture classification without the need for an additional texture feature extraction module. The model was validated for both binary and multiclass categorization of malignant images. The proposed model's classification accuracy ranges from 94.65% to 100% for binary and multiclass classification, and the error rate is 2.78%. Overall, the suggested model has the potential to improve the survival of breast cancer patients by allowing precise diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Objective: To examine the psychosocial issues that emerge in the patients and their spouses upon cancer diagnosis and during its treatment. Furthermore, to search the immigrant specific supportive approaches when providing cancer care. Study Design: Systematic Literature Review. Period: 2008 to 2020. Material & Methods: Thirty-three articles, which met the pre-set criteria, were analyzed, and employed as a reference in this paper. Results: The analysis of the literature reported that depression, anxiety and low quality of life are prevalent among cancer patients and their spouses. The well-established evidence strengthened that culturally competent care, social, as well as linguistic support, are the immigrant tailored strategies that can help satisfy the need of this vulnerable population. Conclusion: The key research findings presented in the paper concludes that immigrants experiencing cancer and their spouses are more prone to acquire psychosocial issues due to their unprecedented circumstances that demand specific immigrants' tailored approaches. [ABSTRACT FROM AUTHOR]

The increase in the prevalence of both colon and rectal cancer in recent years poses challenges for the medical system in terms of patient management and indirectly incurs significant financial burdens. Purpose: The aim of this paper is to track the changes in the prevalance of the colon and rectal cancer at a tertiary clinic in Romania over time and to identify complementary methods to improve the prognosis and quality of life of cancer patients. Material and methods: We conducted an observational, longitudinal, population-based study, including all patients newly diagnosed with colon or rectal neoplasia within the time frame from 1 January 2013 to 1 January 2024 in a tertiary medical clinic in Romania. For each case included in the study, we gathered demographic data (age at the time of cancer diagnosis, gender, place of origin), location of the tumor, duration until surgical intervention, alternative treatment methods employed (such as radiation or chemotherapy, and immunotherapy), and the length of survival. We also assess the feasiblity of physical prehabilitation and rehabilitation programs for inpatients diagnosed with malignant neoplasms of the colon or rectum. Results: The study found significant differences in patient ages and the execution of prehabilitation and rehabilitation practices between those admitted for colon and rectal cancer during the periods 2013-2018 and 2019-2023, with a notable shift in the prevalence of colon versus rectal cancer over these periods. Conclusions: Prehabilitation and rehabilitation practices for colorectal cancer patients are underdocumented or suboptimal, with recent improvements in documentation, especially for rectal cancer due to colostomy needs, and an observed increase in patient age due to COVID-19 pandemic protocols. Additional research and the development of standardized protocols are needed. [ABSTRACT FROM AUTHOR]

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The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet. [ABSTRACT FROM AUTHOR]

Background: Capture of cancer stage at diagnosis is important yet poorly reported by health services to population-based cancer registries. In this paper we describe current completeness of stage information for endometrial cancer available in Australian cancer registries; and develop and validate a set of rules to enable cancer registry medical coders to calculate stage using data available to them (registry-derived stage or 'RD-Stage'). Methodology: Rules for deriving RD-stage (Endometrial carcinoma) were developed using the American Joint Commission on Cancer (AJCC) TNM (tumour, nodes, metastasis) Staging System (8th Edition). An expert working group comprising cancer specialists responsible for delivering cancer care, epidemiologists and medical coders reviewed and endorsed the rules. Baseline completeness of data fields required to calculate RD-Stage, and calculation of the proportion of cases for whom an RD stage could be assigned, was assessed across each Australian jurisdiction. RD-Stage (Endometrial cancer) was calculated by Victorian Cancer Registry (VCR) medical coders and compared with clinical stage recorded by the patient's treating clinician and captured in the National Gynae-Oncology Registry (NGOR). Results: The necessary data completeness level for calculating RD-Stage (Endometrial carcinoma) across various Australian jurisdictions varied from 0 to 89%. Three jurisdictions captured degree of spread of cancer, rendering RD-Stage unable to be calculated. RD-Stage (Endometrial carcinoma) could not be derived for 64/485 (13%) cases and was not captured for 44/485 (9%) cases in NGOR. At stage category level (I, II, III, IV), there was concordance between RD-Stage and NGOR captured stage in 393/410 (96%) of cases (95.8%, Kendall's coefficient = 0.95). Conclusion: A lack of consistency in data captured by, and data sources reporting to, population-based cancer registries meant that it was not possible to provide national endometrial carcinoma stage data at diagnosis. In a sample of Victorian cases, where surgical pathology was available, there was very good concordance between RD-Stage (Endometrial carcinoma) and clinician-recorded stage data available from NGOR. RD-Stage offers promise in capturing endometrial cancer stage at diagnosis for population epidemiological purposes when it is not provided by health services, but requires more extensive validation. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has brought significant disruptions to the landscape of oncological medicine, resulting in postponed cancer diagnoses and treatments, poor implementation of prehabilitation programmes, and an overall altered general care. Purpose: This paper aims to evaluate the impact of the COVID-19 pandemic on the profile of patients admitted to a clinical Hospital from Transylvania, and to evaluate its detrimental effect on the implementation of prehabilitation/rehabilitation programs for patients diagnosed with cancer. Material and methods: The study compared two groups of patients, the ones admitted to the hospital in the year before the pandemic (2019) and the others admitted in 2020. We recorded the number of new cancer diagnoses, the performed interventions of prehabilitation/rehabilitation and we compared the results. Results: The diagnosis of cancer was statistically significantly more frequent in the pandemic year 2020 compared to the same timeframe in 2019. Additionally, fewer interventions of physical prehabilitation or rehabilitation were applied during the pandemic period. Conclusions: The COVID-19 pandemic had a detrimental effect on the cancer patient's benefit from prehabilitation/rehabilitation interventions. [ABSTRACT FROM AUTHOR]

Effective cancer diagnosis, treatment and control depend on interactions among numerous distinct factors, from technology to data to skills to sociology. But a crucial influence is the extent to which the health system takes account of the distinct perspectives of the many different groups of interdependent stakeholders concerned with cancer, including patients, practitioners and planners. This paper provides some elucidation as to how far and how efficiently these interactions currently take place in Europe. It also makes some tentative suggestions as to how conscious systematic interventions could improve cancer outcomes. It is based on a series of expert panels and surveys conducted by the European Alliance for Personalised Medicine (EAPM) that provided information at the national level on three selected parameters: implementation of next-generation sequencing (NGS) and liquid biopsy (LB), attitudes of patients to prevention and practices of sharing genomic data among healthcare professionals (HCPs). The varying data infrastructure highlights the urgent need for substantial improvements to accommodate the increasing importance of genomics data in cancer diagnosis and care. Additionally, we identify disparities in age-specific approaches to cancer prevention, emphasising the necessity for tailored strategies to address unique age group perspectives. Moreover, distinct regional prioritizations in cancer treatment underscore the importance of considering regional variations when shaping future cancer care strategies. This study advocates for collaborative data sharing supported by technological innovation to overcome these challenges, ultimately fostering a holistic and equitable provision of cancer care in Europe. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in using nanoparticles in the medical sciences. Today, metal nanoparticles have many applications in medicine for tumor visualization, drug delivery, and early diagnosis, with different modalities such as X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), etc., and treatment with radiation. This paper reviews recent findings of recent metal nanotheranostics in medical imaging and therapy. The study offers some critical insights into using different types of metal nanoparticles in medicine for cancer detection and treatment purposes. The data of this review study were gathered from multiple scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up through the end of January 2023. In the literature, many metal nanoparticles are used for medical applications. However, due to their high abundance, low price, and high performance for visualization and treatment, nanoparticles such as gold, bismuth, tungsten, tantalum, ytterbium, gadolinium, silver, iron, platinum, and lead have been investigated in this review study. This paper has highlighted the importance of gold, gadolinium, and iron-based metal nanoparticles in different forms for tumor visualization and treatment in medical applications due to their ease of functionalization, low toxicity, and superior biocompatibility. [ABSTRACT FROM AUTHOR]

Background: Patient and public involvement is increasingly considered important in health research. This paper reflects, from both academic and lived experience perspectives, on involving people with lived experience in a study exploring cancer care in prison and how by doing this it enriched the research process. Methods: This paper is based on written and verbal reflections of the lived experience researchers and academic researchers involved in a study exploring the diagnosis and treatment of people with cancer in prison. The study comprised interviews with people with cancer in prison, prison healthcare staff, oncology specialists and custodial staff. Lived experience researchers were involved throughout the research process, including co-conducting interviews with patients and analysing interviews. Results: This paper highlights the importance and value of including lived experience researchers across the research process. We reflect on how lived experience of prison shapes the experience of conducting interviews and analysing data gathered in prison. We reflect on the working relationships between academic and lived experience researchers. We demonstrate how prison research is challenging, but collaboration between lived experience and academic researchers can help to better prepare for the field, to ask more meaningful questions and to create rapport with participants. These types of collaborations can be powerful avenues for skill development for both academic and lived experience researchers, but they require an investment of time and a willingness for shared learning. Conclusions: For academics and lived experience researchers to collaborate successfully and meaningfully care needs to be taken to develop open, honest and equal working relationships. Skills development for academic and lived experience researchers is important. A commitment to building and maintaining relationships is crucial. Having a third party as a mediator can facilitate and foster these relationships. Particularly with people with lived experience of prison it is essential to put the 'do no harm' principle into practice and to have support in place to minimise this. Plain English summary: While patient and public involvement is increasingly considered important in health research, very few papers reflect on the process of collaborating with people with lived experience of prison in health research. This paper is based on written and verbal reflections of both the lived experience and academic researchers on the project that explored how cancer is diagnosed and treated in prison. For academics and lived experience researchers to collaborate successfully and meaningfully care needs to be taken to develop open, honest and equal working relationships. Skills development for academic and lived experience researchers is important. A commitment to building and maintaining relationships is crucial. Having a third party as a mediator can facilitate and foster these relationships. Particularly with people with lived experience of prison it is essential to put the 'do no harm' principle into practice and to have support in place to minimise this. The process of writing this paper provided additional opportunities to reflect on the collaboration which we all found vital. Involving lived experience researchers on all aspects of the research process can strengthen the design, relevancy and outcomes of studies. The process, however, can be emotionally challenging for lived experience and academic researchers, underscoring the need for space for open and honest reflection and learning. Particularly for people with lived experience of prison, being involved in research studies can be a great source of personal growth as it offers an opportunity to use and reframe their, often traumatic, lived experience in a positive way. [ABSTRACT FROM AUTHOR]

Previous research demonstrated that the cancer diagnosis and treatment evoke existential concerns, especially ones related to meaning in life and meaning-making processes. The cancer experience is a vital challenge that often entails changes in what is personally important in life. Firstly, this paper collects evidence supporting that meaning adaptability, the way people adapt their meaning in life to the cancer experience, is a central element in the mental health of cancer patients. Various theories that could account for this meaning adaptability are introduced. Secondly, the paper provides a compilation of studies showing how people change what is significant in their lives within the course of cancer. Finally, the paper presents the available psychological therapies directed to facilitate meaning adaptability in this population. Meaning-centered interventions have been demonstrated to be effective in alleviating the suffering related to the cancer experience and promoting personal growth. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems (CADs) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel end-to-end deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predicts diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path, called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL), focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the cancer semantics and cancer representations are well learned, and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. In addition, by integrating an automatic detection set-up, the DualCoreNet achieves fully automatic breast cancer diagnosis practically. Experimental results show that in benchmark DDSM dataset, DualCoreNet has outperformed other related works in both segmentation and classification tasks, achieving 92.27% DI coefficient and 0.85 AUC score. In another benchmark INbreast dataset, DualCoreNet achieves the best mammography segmentation (93.69% DI coefficient) and competitive classification performance (0.93 AUC score). [ABSTRACT FROM AUTHOR]

Aims and method: This narrative review updates the evidence base for cancer-related post-traumatic stress disorder (PTSD). Databases were searched in December 2021, and included EMBASE, Medline, PsycINFO and PubMed. Adults diagnosed with cancer who had symptoms of PTSD were included. Results: The initial search identified 182 records, and 11 studies were included in the final review. Psychological interventions were varied, and cognitive–behavioural therapy and eye movement desensitisation and reprocessing were perceived to be most efficacious. The studies were also independently rated for methodological quality, which was found to be hugely variable. Clinical implications: There remains a lack of high-quality intervention studies for PTSD in cancer, and there is a wide range of approaches to managing these conditions, with a large heterogeneity in the cancer populations examined and methodologies used. Specific studies designed with patient and public engagement and that tailor the PTSD intervention to particular cancer populations under investigation are required. [ABSTRACT FROM AUTHOR]

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment. [ABSTRACT FROM AUTHOR]

Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system. [ABSTRACT FROM AUTHOR]

Cancer is the second leading cause of death globally and early diagnosis is the best strategy to reduce mortality risk. Biosensors to detect cancer biomarkers are based on various principles of detection, including electrochemical, optical, electrical, and mechanical measurements. Despite the advances in the identification of biomarkers and the conventional 2D manufacturing processes, detection methods for cancers still require improvements in terms of selectivity and sensitivity, especially for point-of-care diagnosis. Three-dimensional printing may offer the features to produce complex geometries in the design of high-precision, low-cost sensors. Three-dimensional printing, also known as additive manufacturing, allows for the production of sensitive, user-friendly, and semi-automated sensors, whose composition, geometry, and functionality can be controlled. This paper reviews the recent use of 3D printing in biosensors for cancer diagnosis, highlighting the main advantages and advances achieved with this technology. Additionally, the challenges in 3D printing technology for the mass production of high-performance biosensors for cancer diagnosis are addressed. [ABSTRACT FROM AUTHOR]

We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. First, some organs (e.g., the pancreas), are highly variable in both anatomy and geometry, and thus very difficult to depict. Second, the neoplasms often vary a lot in its size, shape, as well as its location within the organ. Third, the targets (organs and neoplasms) can be considerably small compared to the human body, and so standard deep networks for segmentation are often less sensitive to these targets and thus predict less accurately especially around their boundaries. In this paper, we present an end-to-end framework named recurrent saliency transformation network (RSTN) for segmenting tiny and/or variable targets. The RSTN is a coarse-to-fine approach that uses prediction from the first (coarse) stage to shrink the input region for the second (fine) stage. A saliency transformation module is inserted between these two stages so that 1) the coarse-scaled segmentation mask can be transferred as spatial weights and applied to the fine stage and 2) the gradients can be back-propagated from the loss layer to the entire network so that the two stages are optimized in a joint manner. In the testing stage, we perform segmentation iteratively to improve accuracy. In this extended journal paper, we allow a gradual optimization to improve the stability of the RSTN, and introduce a hierarchical version named H-RSTN to segment tiny and variable neoplasms such as pancreatic cysts. Experiments are performed on several CT datasets including a public pancreas segmentation dataset, our own multi-organ dataset, and a cystic pancreas dataset. In all these cases, the RSTN outperforms the baseline (a stage-wise coarse-to-fine approach) significantly. Confirmed by the radiologists in our team, these promising segmentation results can help early diagnosis of pancreatic cancer. The code and pre-trained models of our project were made available at https://github.com/198808xc/OrganSegRSTN. [ABSTRACT FROM AUTHOR]

Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further treatment of cancer patients. According to statistics, about 90% of cancer-related deaths are due to metastasis of primary tumor cells. Therefore, the study of the location of cancer recurrence and its influencing factors is of great significance for the clinical diagnosis and treatment of cancer. In this paper, we propose an assisted diagnosis model for cancer patients based on federated learning. In terms of data, the influencing factors of cancer recurrence and the special needs of data samples required by federated learning were comprehensively considered. Six first-level impact indicators were determined, and the historical case data of cancer patients were further collected. Based on the federated learning framework combined with convolutional neural network, various physical examination indicators of patients were taken as input. The recurrence time and recurrence location of patients were used as output to construct an auxiliary diagnostic model, and linear regression, support vector regression, Bayesling regression, gradient ascending tree and multilayer perceptrons neural network algorithm were used as comparison algorithms. CNN's federated prediction model based on improved under the condition of the joint modeling and simulation on the five types of cancer data accuracy reached more than 90%, the accuracy is better than single modeling machine learning tree model and linear model and neural network, the results show that auxiliary diagnosis model based on the study of cancer patients in assisted the doctor in the diagnosis of patients, As well as effectively provide nutritional programs for patients and have application value in prolonging the life of patients, it has certain guiding significance in the field of medical cancer rehabilitation. [ABSTRACT FROM AUTHOR]

This paper introduces a configurable combined laser, ultrasound, and elasticity (CLUE) imaging platform. The CLUE platform enables imaging sequences capable of simultaneously providing quantitative acoustic, optical, and mechanical contrast for comprehensive diagnosis and monitoring of complex diseases, such as cancer. The CLUE imaging platform was developed on a Verasonics ultrasound scanner integrated with a pulsed laser, and it was designed to be modular and scalable to allow researchers to create their own specific imaging sequences efficiently. The CLUE imaging platform and sequence were demonstrated in a tissue-mimicking phantom containing a stiff inclusion labeled with optically-activated nanodroplets and in an ex vivo mouse spleen. We have shown that CLUE imaging can simultaneously capture multi-functional imaging signals providing quantitative information on tissue. [ABSTRACT FROM AUTHOR]

The high mobility group A1 (HMGA1) gene plays an important role in numerous malignant cancers. HMGA1 is an oncofoetal gene, and we have a certain understanding of the biological function of HMGA1 based on its activities in various neoplasms. As an architectural transcription factor, HMGA1 remodels the chromatin structure and promotes the interaction between transcriptional regulatory proteins and DNA in different cancers. Through analysis of the molecular mechanism of HMGA1 and clinical studies, emerging evidence indicates that HMGA1 promotes the occurrence and metastasis of cancer. Within a similar location or the same genetic background, the function and role of HMGA1 may have certain similarities. In this paper, to characterize HMGA1 comprehensively, research on various types of tumours is discussed to further understanding of the function and mechanism of HMGA1. The findings provide a more reliable basis for classifying HMGA1 function according to the tumour location. In this review, we summarize recent studies related to HMGA1, including its structure and oncogenic properties, its major functions in each cancer, its upstream and downstream regulation associated with the tumourigenesis and metastasis of cancer, and its potential as a biomarker for clinical diagnosis of cancer. [ABSTRACT FROM AUTHOR]

Recently, cancer has been characterized as a heterogeneous disease composed of many different subtypes. Early diagnosis of cancer subtypes is an important study of cancer research, which can be of tremendous help to patients after treatment. In this paper, we first extract a novel dataset, which contains gene expression, miRNA expression, and isoform expression of five cancers from The Cancer Genome Atlas (TCGA). Next, to avoid the effect of noise existing in 60, 483 genes, we select a small number of genes by using LASSO that employs gene expression and survival time of patients. Then, we construct one similarity kernel for each expression data by using Chebyshev distance. And also, We used SKF to fused the three similarity matrix composed of gene, Iso, and miRNA, and finally clustered the fused similarity matrix with spectral clustering. In the experimental results, our method has better P -value in the Cox model than other methods on 10 cancer data from Jiang Dataset and Novel Dataset. We have drawn different survival curves for different cancers and found that some genes play a key role in cancer. For breast cancer, we find out that HSPA2A, RNASE1, CLIC6, and IFITM1 are highly expressed in some specific groups. For lung cancer, we ensure that C4BPA, SESN3, and IRS1 are highly expressed in some specific groups. The code and all supporting data files are available from https://github.com/guofei-tju/Uncovering-Cancer-Subtypes-via-LASSO. [ABSTRACT FROM AUTHOR]

During the initial consultation with a patient to communicate a diagnosis of late-stage cancer, the oncologist may refrain from giving survival statistics, redirecting the conversation from the bad news (incurability) to the practical aspects of the patient's care (treatments, timetables, appointments, and testing to monitor response to treatment). Whether conscious or unconscious, this diversion helps cushion the impact of the disturbing news. This paper shows that clinicians' gingerly handling of harsh facts when they talk with patients also applies to health educators and researchers when they write about late-stage cancer. As a result, these cancer patients typically lack an understanding of their poor prognosis and the limited effectiveness of most available treatments, possibly compromising their ability to make informed choices. To remedy this problem, I describe an approach to straight talk about late-stage cancer that can give a patient realistic hopes instead of false hopes that are apt to betray later on. I also propose an enhanced method of displaying and interpreting comparative efficacy data that can facilitate understanding and serve as a basis for shared decision making. [ABSTRACT FROM AUTHOR]

Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician's screen during the physician-patient interaction, they will be an integral part of physician--patient-shared decision-making, and they will improve clinical care and patient outcomes. [ABSTRACT FROM AUTHOR]

Skin cancer is common and deadly, needs to be detected and treated properly. Deep learning algorithms like UNet have shown potential results in medical imaging. Such approaches still struggle to capture fine‐grained details and scale differences in skin lesions‐based occlusions' appearance, size etc. This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and global context. Attention approaches also help the network to suppress noise by focusing on informative features. We have evaluated MSPAN model on the publicly available ISIC2018 benchmark dataset for skin lesion segmentation. The method surpasses traditional UNet and other current methods in accuracy and effectiveness. The model also has a post‐processing to estimate lesion area for fast inference, making it suitable for extensive screening. Redesigned UNet with the Multi‐Scale Pyramid Attention Network improves skin cancer lesion segmentation. The model's ability to collect fine‐grained information and handle occlusions allows for more accurate skin cancer diagnosis and treatment. The MSPAN design can improve computer‐aided diagnosis systems and help dermatologists make precise clinical decisions. [ABSTRACT FROM AUTHOR]

Introduction: To investigate the occurrence of previous cancer diagnoses in women suffering from premature ovarian insufficiency (POI) and compare it with the general population, shedding light on the association between cancer, cancer treatments, and POI. Material and methods: We conducted a nationwide case–control study based on registry data from various sources, including the Social Insurance Institution, Finnish Population Information System, and Finnish Cancer Registry spanning from 1953 to 2018. Our participants comprised all women in Finland who, between 1988 and 2017, received hormone replacement therapy reimbursement for ovarian insufficiency before the age of 40 years (n = 5221). Controls, matched in terms of age and municipality of residence, were selected from the Finnish Population Information System (n = 20 822). Our main exposure variable was a history of cancer diagnosis preceding the diagnosis of POI. We analyzed odds ratios (OR) to compare the prevalence of previous cancers in women with POI with that in controls, stratifying results based on cancer type, age at cancer diagnosis, and the time interval between cancer diagnosis and POI. We also assessed changes in OR for previous cancer diagnoses over the follow‐up period. Results: Out of the women diagnosed with POI, 21.9% had previously been diagnosed with cancer, resulting in an elevated OR of 36.5 (95% confidence interval [CI] 30.9 to 43.3) compared with 0.8% of the controls. The risk of developing POI was most pronounced during the first 2 years following a cancer diagnosis, with an OR of 103 (95% CI 74.1 to 144). Importantly, this risk remained elevated even when the time interval between cancer and POI exceeded 10 years, with an OR of 5.40 (95% CI 3.54 to 8.23). Conclusions: This study reveals that 21.9% of women with POI have a history of cancer, making the prevalence of cancer among these women 27.5 times higher than age‐matched controls in the Finnish population. The risk of developing POI is most substantial in the first 2 years following a cancer diagnosis. These findings underscore the role of cancer treatments as an etiological factor for POI and emphasize the importance of recognizing the risk of POI in cancer survivors for early diagnosis and intervention. [ABSTRACT FROM AUTHOR]

The study aims to build a mobile-based application prototype with an Android platform for learning early cancer detection. This prototype design is emerged from data collection about the challenges faced by health workers to gain knowledge about cancer. It requires mobile-based learning technology to assist in their daily work. Based on the data gathered, an Android-based application is developed to be used to simplify the knowledge dissemination method and enhance the material quality by digitizing content into multimedia formats. [ABSTRACT FROM AUTHOR]

Background: Direct access (DA) testing allows GPs to refer patients for investigation without consulting a specialist. The aim is to reduce waiting time for investigations and unnecessary appointments, enabling treatment to begin without delay.Aim: To establish the proportion of patients diagnosed with cancer and other diseases through DA testing, time to diagnosis, and suitability of DA investigations.Design and Setting: Systematic review assessing the effectiveness of GP DA testing in adults.Method: MEDLINE, Embase, and the Cochrane Library were searched. Where possible, study data were pooled and analysed quantitatively. Where this was not possible, the data are presented narratively.Results: The authors identified 60 papers that met pre-specified inclusion criteria. Most studies were carried out in the UK and were judged to be of poor quality. The authors found no significant difference in the pooled cancer conversion rate between GP DA referrals and patients who first consulted a specialist for any test, except gastroscopy. There were also no significant differences in the proportions of patients receiving any non-cancer diagnosis. Referrals for testing were deemed appropriate in 66.4% of those coming from GPs, and in 80.9% of those from consultants; this difference was not significant. The time from referral to testing was significantly shorter for patients referred for DA tests. Patient and GP satisfaction with DA testing was consistently high.Conclusion: GP DA testing performs as well as, and on some measures better than, consultant triaged testing on measures of disease detection, appropriateness of referrals, interval from referral to testing, and patient and GP satisfaction. [ABSTRACT FROM AUTHOR]

Cancer and heart failure (HF) are common medical conditions with a steadily rising prevalence in industrialized countries, particularly in the elderly, and they both potentially carry a poor prognosis. A new diagnosis of malignancy in subjects with pre-existing HF is not infrequent, and challenges HF specialists as well as oncologists with complex questions relating to both HF and cancer management. An increased incidence of cancer in patients with established HF has also been suggested. This review paper summarizes the epidemiology and the prognostic implications of cancer occurrence in HF, the impact of pre-existing HF on cancer treatment decisions and the impact of cancer on HF therapeutic options, while providing some practical suggestions regarding patient care and highlighting gaps in knowledge. [ABSTRACT FROM AUTHOR]

Background: The presence of circulating cell-free DNA from tumours in blood (ctDNA) is of major importance to those interested in early cancer detection, as well as to those wishing to monitor tumour progression or diagnose the presence of activating mutations to guide treatment. In 2014, the UK Early Cancer Detection Consortium undertook a systematic mapping review of the literature to identify blood-based biomarkers with potential for the development of a non-invasive blood test for cancer screening, and which identified this as a major area of interest. This review builds on the mapping review to expand the ctDNA dataset to examine the best options for the detection of multiple cancer types.Methods: The original mapping review was based on comprehensive searches of the electronic databases Medline, Embase, CINAHL, the Cochrane library, and Biosis to obtain relevant literature on blood-based biomarkers for cancer detection in humans (PROSPERO no. CRD42014010827). The abstracts for each paper were reviewed to determine whether validation data were reported, and then examined in full. Publications concentrating on monitoring of disease burden or mutations were excluded.Results: The search identified 94 ctDNA studies meeting the criteria for review. All but 5 studies examined one cancer type, with breast, colorectal and lung cancers representing 60% of studies. The size and design of the studies varied widely. Controls were included in 77% of publications. The largest study included 640 patients, but the median study size was 65 cases and 35 controls, and the bulk of studies (71%) included less than 100 patients. Studies either estimated cfDNA levels non-specifically or tested for cancer-specific mutations or methylation changes (the majority using PCR-based methods).Conclusion: We have systematically reviewed ctDNA blood biomarkers for the early detection of cancer. Pre-analytical, analytical, and post-analytical considerations were identified which need to be addressed before such biomarkers enter clinical practice. The value of small studies with no comparison between methods, or even the inclusion of controls is highly questionable, and larger validation studies will be required before such methods can be considered for early cancer detection. [ABSTRACT FROM AUTHOR]

Previous studies by our group have shown that 3-D high-frequency quantitative ultrasound (QUS) methods have the potential to differentiate metastatic lymph nodes (LNs) from cancer-free LNs dissected from human cancer patients. To successfully perform these methods inside the LN parenchyma (LNP), an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from QUS processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of LNs, the intensity distribution of LNP and fat varies spatially because of acoustic attenuation and focusing effects. Thus, the intensity contrast between two object regions (e.g., LNP and fat) is also spatially varying. In our previous work, nested graph cut (GC) demonstrated its ability to simultaneously segment LNP, fat, and the outer phosphate-buffered saline bath even when some boundaries are lost because of acoustic attenuation and focusing effects. This paper describes a novel approach called GC with locally adaptive energy to further deal with spatially varying distributions of LNP and fat caused by inhomogeneous acoustic attenuation. The proposed method achieved Dice similarity coefficients of 0.937±0.035 when compared with expert manual segmentation on a representative data set consisting of 115 3-D LN images obtained from colorectal cancer patients. [ABSTRACT FROM AUTHOR]

Hyperthermia is an emerging cancer treatment modality, which involves applying heat to the malignant tumor. The heating can be delivered using electromagnetic (EM) energy, mostly in the radiofrequency (RF) or microwave range. Accurate patient-specific hyperthermia treatment planning (HTP) is essential for effective and safe treatments, in particular, for deep and loco-regional hyperthermia. An important aspect of HTP is the ability to focus microwave energy into the tumor and reduce the occurrence of hot spots in healthy tissue. This paper presents a method for optimizing the specific absorption rate (SAR) distribution for the head and neck cancer hyperthermia treatment. The SAR quantifies the rate at which localized RF or microwave energy is absorbed by the biological tissue when exposed to an EM field. A differential evolution (DE) optimization algorithm is proposed in order to improve the SAR coverage of the target region. The efficacy of the proposed algorithm is demonstrated by testing with the Erasmus MC patient dataset. DE is compared to the particle swarm optimization (PSO) method, in terms of average performance and standard deviation and across various clinical metrics, such as the hot-spot-tumor SAR quotient (HTQ), treatment quantifiers, and temperature parameters. While hot spots in the SAR distribution remain a problem with current approaches, DE enhances focusing microwave energy absorption to the target region during hyperthermia treatment. In particular, DE offers improved performance compared to the PSO algorithm currently deployed in the clinic, reporting a range of improvement of HTQ standard deviation of between 40.1–96.8% across six patients. [ABSTRACT FROM PUBLISHER]

Objective: False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively address the challenges in this task and therefore to accurately discriminate the true nodules from a large number of candidates. Methods: We propose a novel method employing three-dimensional (3-D) convolutional neural networks (CNNs) for false positive reduction in automated pulmonary nodule detection from volumetric computed tomography (CT) scans. Compared with its 2-D counterparts, the 3-D CNNs can encode richer spatial information and extract more representative features via their hierarchical architecture trained with 3-D samples. More importantly, we further propose a simple yet effective strategy to encode multilevel contextual information to meet the challenges coming with the large variations and hard mimics of pulmonary nodules. Results: The proposed framework has been extensively validated in the LUNA16 challenge held in conjunction with ISBI 2016, where we achieved the highest competition performance metric (CPM) score in the false positive reduction track. Conclusion: Experimental results demonstrated the importance and effectiveness of integrating multilevel contextual information into 3-D CNN framework for automated pulmonary nodule detection in volumetric CT data. Significance: While our method is tailored for pulmonary nodule detection, the proposed framework is general and can be easily extended to many other 3-D object detection tasks from volumetric medical images, where the targeting objects have large variations and are accompanied by a number of hard mimics. [ABSTRACT FROM AUTHOR]

Background: A large volume of research has been published on both the socio economic and demographic determinants of cancer and on the health of immigrants and minority groups. Yet because of data limitations, little research examines differences in the occurrence of cancer incidence between immigrants and non-immigrants and among immigrants defined by region of birth and time in the host country. In particular it is not known whether a healthy immigrant effect is present for cancer and if so, whether this advantage is lost with additional years of residence in the host country.Methods: This paper uses a large data file from Statistics Canada that links Census information on immigrant status, socioeconomic status including educational attainment, and other person-level information with administrative data on cancer and mortality over a continuous 13 year period of observation. It estimates discrete and continuous time duration models to identify differences in cancer diagnosis by immigrant subgroup after controlling for a variety of potential confounders. Differences in historical smoking behavior are not observable at the individual level in the dataset but are accounted for indirectly using various methods.Results: Results in general confirm the existence of a healthy immigrant effect for cancer in that, overall, recent immigrants to Canada are significantly less likely than otherwise comparable non-immigrant Canadians to be diagnosed with any cancer and the most common forms of cancer by site. As well, this gap appears to decline with additional years in Canada for immigrant men and women, eventually converging to Canadian-born levels. Differentiating among immigrant subgroups by period of arrival and country of birth reveals significant variation across immigrant subgroups, with immigrant men and women from developing countries typically having a lower likelihood of being diagnosed with cancer than immigrants from the US, UK and continental Europe. As well, controlling for immigrant heterogeneity this way weakens the conclusion that the gap narrows with years in Canada. Immigrant men overall continue to exhibit convergence to Canadian-born levels for diagnosis of any cancer and for prostate cancer, while immigrant women exhibit narrowing over time only for breast cancer. Although smoking behavior is not directly observed, controlling for subgroup-specific lifetime smoking behavior using survey data has only a relatively minor effect on the estimated differences.Conclusions: The specificity of the results by cancer type, gender, immigrant status and ethnicity provides useful guidance for future research by helping to narrow the possible channels through which social and economic characteristics may be affecting cancer incidence. [ABSTRACT FROM AUTHOR]

Background: A better understanding of health-related quality of life (HRQoL) and psychological distress in cancer survivors can raise awareness, promote the development of policies in cancer survivorship care, and facilitate better targeted use of limited resources in low- and middle-income countries (LMICs). The main objectives of this paper were therefore to assess HRQoL and the prevalence of psychological distress amongst cancer survivors in Southeast Asia and identify risk factors of these outcomes.Methods: The ACTION study was a longitudinal study in eight LMICs in Southeast Asia with 5249 first time cancer survivors followed up at 1 year after diagnosis. HRQoL was assessed using the EORTC QLQ-C30 and EQ-5D. Psychological distress (anxiety and depression) was assessed using the Hospital Anxiety and Depression Scale. General linear models and multiple logistic regression were used to identify independent predictors of HRQoL and psychological distress.Results: One year after diagnosis, the mean EORTC QLQ-C30 global health score for survivors was 66.2 out of 100 (SD 22.0), the mean index score on the EQ-5D was 0.74 (SD 0.23), 37% of survivors had at least mild levels of anxiety, and 46% showed at least mild levels of depression. Poorest HRQoL and highest prevalence of anxiety and depression were seen in patients with lung cancer and lymphomas, while highest scores and least psychological distress were seen in female patients with breast and cervical cancer. The most significant predictor of poor HRQoL and psychological distress outcomes was cancer stage at diagnosis. Age, co-morbidities, treatment, and several socioeconomic factors were associated with HRQoL and psychological distress.Conclusions: Cancer survivors in LMICs in Southeast Asia have impaired HRQoL and substantial proportions have psychological distress. Patients with advanced cancer stages at diagnosis and those in a poor socioeconomic position were most at risk of such poor outcomes. Supportive interventions for cancer patients that address wider aspects of patient wellbeing are needed, as well as policies that address financial and other barriers to timely treatment. [ABSTRACT FROM AUTHOR]

The increasing knowledge of the molecular pathogenesis of cancer and the rapid development of new molecular techniques are promoting the study of early molecular alterations involved in cancer development in body fluids. Specific genetic and epigenetic alterations could be found in plasma, serum, and urine cell-free DNA (cfDNA) and could potentially be used as diagnostic biomarkers for several types of cancers. This review focuses on the role of cfDNA in diagnosis: a PubMed search was performed by selecting papers according to journal impact factor and robustness of statistical analysis. A comprehensive evaluation of "liquid biopsy", including cfDNA analysis, will be one of the critical challenges to better understand the early mechanisms of cancer development. [ABSTRACT FROM AUTHOR]

Plain English summary: This paper describes the methods that were used to develop a health behaviour intervention specifically for teenage and young adult cancer survivors (TYACS). The program of work, carried out in partnership with CLIC Sargent (a UK based cancer charity for children and young people) was guided by The Behaviour Change Intervention Design Process. A systematic review of existing intervention studies was carried out and TYACS were surveyed on their interest in receiving health behaviour information and their preference regarding the format, delivery and timing of such information. Health professionals were also surveyed to gather their views on how health behaviour information would be best delivered to young people with cancer. The results of these studies informed the development of a collection of health behaviour change intervention resources containing comprehensive lifestyle information and behaviour change support tools. TYACS and TYA health professionals were invited to review and provide feedback on the relevance, appeal and usability of the resources. It is hoped that by involving TYACS and TYA representatives at every stage of intervention development the problem of low uptake and adherence commonly encountered during intervention piloting will be prevented. Background Teenage and Young Adult Cancer Survivors (TYACS) are advised to adopt a healthy lifestyle in order to reduce the impact of cancer and its treatments upon their long-term health. However, at present there are no interventions available in the UK to support TYACS to lead a healthy lifestyle. To inform the development of a lifestyle intervention for TYACS a partnership was set up between academic behavioural scientists and CLIC Sargent, a cancer charity which supports children and young people. Methods A series of studies to understand patient and professionals needs and perspectives regarding health behaviour change were carried out. TYACS were surveyed to gather data on their current health behaviour status; interest in, and experience of receiving, lifestyle advice; and preference regarding the type, format, and delivery of a lifestyle intervention. Health care professionals were surveyed simultaneously to gather their views on how best to promote health behaviour change to TYACS. In this paper we summarise key findings from the development work, the resulting lifestyle intervention, and new data from a preliminary evaluation study exploring TYACS and TYA health professionals' views on the relevance, appeal and usability of the intervention resources. Results A collection of health behaviour change intervention resources containing lifestyle information and behaviour change support tools were developed. These intervention resources were well received by TYACS and health professionals with the majority rating the information as high quality, helpful and relevant. Over 80% of TYACS reported they would find the support tools 'very appealing' or 'quite appealing'. TYACS and health professionals provided feedback on how the resources could be improved including commenting that more personalized or tailored health behaviour information would be beneficial. Discussion and conclusion It is hoped that by involving TYACS and TYA representatives at every stage of intervention development,and carrying out a preliminary evaluation of the intervention resources, the problem of low uptake and adherence commonly encountered during formal intervention piloting and evaluation will be prevented. [ABSTRACT FROM AUTHOR]

Background: In response to evidence about the health benefits of smoking cessation at time of cancer diagnosis, Ontario Health (Cancer Care Ontario) (OH-CCO) instructed Regional Cancer Centres (RCC) to implement smoking cessation interventions (SCI). RCCs were given flexibility to implement SCIs according to their context but were required to screen new patients for tobacco status, advise patients about the importance of quitting, and refer patients to cessation supports. The purpose of this evaluation was to identify practices that influenced successful implementation across RCCs. Methods: A realist evaluation approach was employed. Realist evaluations examine how underlying processes of an intervention (mechanisms) in specific settings (contexts) interact to produce results (outcomes). A realist evaluation may thus help to generate an understanding of what may or may not work across contexts. Results: The RCCs with the highest Tobacco Screening Rates used a centralized system. Regarding the process for advising and referring, three RCCs offered robust smoking cessation training, resulting in advice and referral rates between 80% and 100%. Five RCCs surpassed the target for Accepted Referral Rates; acceptance rates for internal referral were highest overall. Conclusion: Findings highlight factors that may influence successful SCI implementation. [ABSTRACT FROM AUTHOR]

Introduction: Screening of cancer is the maximum effort and critical element for providing health related care in order to decrease cancer related dealt because of the disease burden is in its advanced stages. Unfortunately, advanced-stage presentation and late diagnosis of cancers endure a problem in low-income countries including Ethiopia. However, there is scarcity of published articles about the problem in Ethiopia. Objective: This study aimed to assess the prevalence of advanced-stage -stage presentation of cancer at the time of diagnosis and associated factors among adult cancer patients at Northwest Amhara comprehensive Specialized Hospitals, oncology treatment units, Northwest Ethiopia, 2022. Methods: An institution based cross-sectional study was conducted in Northwest Amhara public referral hospitals on 422 study participants. A systematic random sampling technique was performed. The data were collected through face to face interview and document review via structured, pretested questionnaires. Epi. Data version 4.6 and Stata version 14.0 software's were used for data entry and analysis respectively. Logistic regression were carried out to recognize factors associated with advanced-stage -stage presentation of cancer at the time of diagnosis. Adjusted odds ratio with a 95% confidence interval were used to measure the strength of association. Variables having p-value less than 0.2 in bivariable analysis were entered in to multivariable analysis; variables with a p-value < 0.05 were declared significantly associated with advanced-stage -stage presentation of cancer at the time of diagnosis. Results: The overall prevalence of advanced-stage presentation of cancer at the time of diagnosis was found to be 67.57%. Age ≥ 60 years old patients (AOR = 6.10, 95%: (1.16–32.1)), patients had have a feeling of burden (AOR = 1.82, 95%, CI: (1.04–3.20)), and cancer patients with comorbidity illness (AOR = 2.40, 95%, CI: (1.40–4.12)) were significantly associated with advanced-stage presentation of cancer at the time of diagnosis. Conclusion: The prevalence of advanced-stage presentation of cancer at the time of diagnosis was found to be high. Its better, health care providers in oncology treatment facilities need to give special attention to older patients, having feeling of burden and cancer patient with comorbidity to reduce the risk of developing late stage presentation of cancer. [ABSTRACT FROM AUTHOR]

Background: Tumor lysis syndrome (TLS) is a well-recognized medical emergency in patients with cancer diagnosis. The diagnostic criteria of TLS have been revised many times since it was recognized, but still have many drawbacks that limit diagnosis accuracy. Summary: Autopsy studies in patients with perimortem diagnoses of TLS have shown that they may not have actually had TLS. Therefore, in many cancer patients who are at risk for TLS, clinical and laboratory criteria may be fulfilled due to other causes of acute kidney injury. In this review, we aimed to cast a spotlight on the shortcomings and pitfalls of the current diagnostic criteria for TLS, and propose a roadmap for developing more rigorous criteria that improve on the diagnostic accuracy. Key Messages: Causes of AKI in patients with cancer other than TLS should be considered. Because current diagnostic criteria may miss those differential diagnosis, specific biomarkers that can tell when TLS is the underlying process are an important need, besides appropriate criteria that can jump over the pitfalls in the current criteria and enhance the recognition of TLS among other causes. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is the most common cancer among Saudi males with age standardized rate (SAR) of 12.9 and 12.5/100,000 in 2016 and 2017, respectively, and the third most common cancer among Saudi females with (SAR) of 9.5 and 10.9/100,000 in 2016 and 2017, respectively. Aim: The aim of this study is to identify the presenting symptoms of CRC in our population and to improve our ability to diagnose it and treat it as early as possible. Materials and Methods: In this retrospective analysis, the medical records of all patients diagnosed with CRC in King Khalid University Hospital during 2016 and 2017 (n = 266), were reviewed. Results: The median age at diagnosis was 59 years (standard deviation = 13.6), 52 (22%) of those patients were younger than 50 years old, and 20 (9.9%) were younger than 40 years old. 137 (58%) of them are males. The most common presenting symptoms were abdominal pain (n = 115, 54.8%), weight loss (n = 96, 45.7%), and hematochezia (n = 88, 41.9), For more than half of the patients, bleeding per rectum was not reported, and for the majority of the patients younger than 50 years old, only (n = 24, 49%) presented with bleeding per rectum, abdominal pain (n = 32, 65.3%) and weight loss (n = 21, 42.9%). 87 patients (48%) had stage III tumor at presentation while 44 (24%) had stage IV. Conclusion: diagnosis of CRC should have a high index of suspicion to diagnose it at an earlier stage and have a better outcome, especially in the younger population. [ABSTRACT FROM AUTHOR]

Background Prior work assessing disparities in cancer outcomes has relied on regional socioeconomic metrics. These metrics average data across many individuals, resulting in a loss of granularity and confounding with other regional factors. Methods Using patients' addresses at the time of diagnosis from the Ohio Cancer Incidence Surveillance System, we retrieved individual home price estimates from an online real estate marketplace. This individual-level estimate was compared with the Area Deprivation Index (ADI) at the census block group level. Multivariable Cox proportional hazards models were used to determine the relationship between home price estimates and all-cause and cancer-specific mortality. Results A total of 667 277 patients in Ohio Cancer Incidence Surveillance System were linked to individual home prices across 16 cancers. Increasing home prices, adjusted for age, stage at diagnosis, and ADI, were associated with a decrease in the hazard of all-cause and cancer-specific mortality (hazard ratio [HR] = 0.92, 95% confidence interval [CI] = 0.92 to 0.93, and HR = 0.95, 95% CI = 0.94 to 0.95, respectively). Following a cancer diagnosis, individuals with home prices 2 standard deviations above the mean had an estimated 10-year survival probability (7.8%, 95% CI = 7.2% to 8.3%) higher than those with home prices 2 standard deviations below the mean. The association between home price and mortality was substantially more prominent for patients living in less deprived census block groups (P interaction <.001) than for those living in more deprived census block groups. Conclusion Higher individual home prices were associated with improved all-cause and cancer-specific mortality, even after accounting for regional measures of deprivation. [ABSTRACT FROM AUTHOR]

Purpose: In order to deliver appropriate and timely care planning and minimise avoidable late diagnoses, clinicians need to be aware of which patients are at higher risk of receiving a late cancer diagnosis. We aimed to determine which demographic and clinical factors are associated with receiving a 'late' cancer diagnosis (within the last 12 weeks of life). Method: Retrospective cohort study of 2,443 people who died from cancer ('cancer decedents') in 2013–2015. Demographic and cancer registry datasets linked using patient-identifying Community Health Index numbers. Analysis used binary logistic regression, with univariate and adjusted odds ratios (SPSS v25). Results: One third (n = 831,34.0%) received a late diagnosis. Age and cancer type were significantly associated with late cancer diagnosis (p < 0.001). Other demographic factors were not associated with receiving a late diagnosis. Cancer decedents with lung cancer (Odds Ratios presented in abstract are the inverse of those presented in the main text, where lung cancer is the reference category. Presented as 1/(OR multivariate)) were more likely to have late diagnosis than those with bowel (95% Confidence Interval [95%CI] Odds Ratio (OR)1.52 (OR1.12 to 2.04)), breast or ovarian (95%CI OR3.33 (OR2.27 to 5.0) or prostate (95%CI OR9.09 (OR4.0 to 20.0)) cancers. Cancer decedents aged > 85 years had higher odds of late diagnosis (95%CI OR3.45 (OR2.63 to 4.55)), compared to those aged < 65 years. Conclusions: Cancer decedents who were older and those with lung cancer were significantly more likely to receive late cancer diagnoses than those who were younger or who had other cancer types. Key messages: • One third (n=831,34.0%) of cancer decedents had late diagnoses (within the last 12 weeks of life). • Cancer decedents with lung cancer had higher odds of late diagnosis than those with other cancers. • Cancer decedents >85 years old were 3 times more likely to have late diagnoses than those <65 years old. [ABSTRACT FROM AUTHOR]

One unexploited family of cancer biomarkers comprise glycoproteins, carbohydrates, and glycolipids (the Tumor Glycocode).A class of glycolipid cancer biomarkers, the tumor-marker gangliosides (TMGs) are presented here as potential diagnostics for detecting cancer, especially at early stages, as the biological function of TMGs makes them etiological. We propose that a quantitative matrix of the Cancer Biomarker Glycocode and artificial intelligence-driven algorithms will expand the menu of validated cancer biomarkers as a step to resolve some of the challenges in cancer diagnosis, and yield a combination that can identify a specific cancer, in a tissue-agnostic manner especially at early stages, to enable early intervention. Diagnosis is critical to reducing cancer mortality but many cancers lack efficient and effective diagnostic tests, especially for early stage disease. Ideal diagnostic biomarkers are etiological, samples are preferably obtained via non-invasive methods (e.g. liquid biopsy of blood or urine), and are quantitated using assays that yield high diagnostic sensitivity and specificity for efficient diagnosis, prognosis, or predicting response to therapy. Validated biomarkers with these features are rare. While the advent of proteomics and genomics has led to the identification of a multitude of proteins and nucleic acid sequences as cancer biomarkers, relatively few have been approved for clinical use. The use of multiplex arrays and artificial intelligence-driven algorithms offer the option of combining data of known biomarkers; however, for most, the sensitivity and the specificity are below acceptable criteria, and clinical validation has proven difficult. One strategic solution to this problem is to expand the biomarker families beyond those currently exploited. One unexploited family of cancer biomarkers comprise glycoproteins, carbohydrates, and glycolipids (the Tumor Glycocode). Here, we focus on a family of glycolipid cancer biomarkers, the tumor-marker gangliosides (TMGs). We discuss the diagnostic potential of TMGs for detecting cancer, especially at early stages. We include prior studies from the literature to summarize findings for ganglioside quantification, expression, detection, and biological function and its role in various cancers. We highlight the examples of TMGs exhibiting ideal properties of cancer diagnostic biomarkers, and the application of GD2 and GD3 for diagnosis of early stage cancers with high sensitivity and specificity. We propose that a quantitative matrix of the Cancer Biomarker Glycocode and artificial intelligence-driven algorithms will expand the menu of validated cancer biomarkers as a step to resolve some of the challenges in cancer diagnosis, and yield a combination that can identify a specific cancer, in a tissue-agnostic manner especially at early stages, to enable early intervention. [ABSTRACT FROM AUTHOR]

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Goal: The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods: An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion: The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance: Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. [ABSTRACT FROM PUBLISHER]

This paper joins a discussion about the representational dissonance and commemorative ethics of two self-referential works that engage with Susan Sontag's 2004 death from Myelodysplastic Syndrome: Annie Leibovitz's A Photographer's Life1990–2005 (2006) and David Rieff's Swimming in a Sea of Death: A Son's Memoir (2008). Instead of approaching these two texts as testimonial accounts measured by standards of reliability and grace, this paper considers how the temporal dissonance produced by an incurable cancer diagnosis thwarts questions of personhood and ethical intention in Leibovitz's photography and Rieff 's prose. By contextualizing these works as the caregivers' experience of Sontag's illness, this paper reads them as attempts at gauging two distinct temporal perspectives that confound identification—those of living through and of remembering terminal time. [ABSTRACT FROM AUTHOR]

Background: In Australia, 40% of people diagnosed with cancer will be of working age (25-64 years). A cancer diagnosis may lead to temporary or permanent changes in a person's labour force participation, which has an economic impact on both the individual and the economy. However, little is known about this economic impact of cancer due to lost productivity in Australia. This paper aims to determine the labour force participation characteristics of people with cancer, to estimate the indirect cost due to lost productivity, and to identify any inequality in the distribution of labour force absence in Australia.Methods: This study used national cross-sectional data from the 2015 Survey of Disability, Ageing and Carers, conducted by the Australian Bureau of Statistics (ABS). The ABS weighted each component of the survey to ensure the sample represented the population distribution of Australia. The analysis was limited to people aged 25-64 years. Participants were assigned to one of three health condition groups: 'no health condition', 'cancer', and 'any other long-term health condition'. A series of logistic regression models were constructed to determine the association between health condition and labour force participation.Results: A total of 34,393 participants surveyed were aged 25-64 years, representing approximately 12,387,800 Australians. Almost half (46%) of people with cancer were not in the labour force, resulting in a reduction of $1.7 billion to the Australian gross domestic product (GDP). Amongst those in the labour force, people with no health condition were 3.00 times more likely to be employed full-time compared to people with cancer (95% CI 1.96-4.57), after adjusting for age, sex, educational attainment and rurality. Amongst those with cancer, people without a tertiary qualification were 3.73 times more likely to be out of the labour force (95% CI 1.97-7.07).Conclusions: This paper is the first in Australia to estimate the national labour force participation rates of people with cancer. People with cancer were less likely to be in the labour force, resulting in a reduction in Australia's GDP. Cancer survivors, especially those without a tertiary qualification may benefit from support to return to work after a diagnosis. [ABSTRACT FROM AUTHOR]

Background: Patient experience is increasingly used as an indicator of high quality care in addition to more traditional clinical end-points. Surveys are generally accepted as appropriate methodology to capture patient experience. No validated patient experience surveys exist specifically for adolescents and young adults (AYA) aged 13-24 years at diagnosis with cancer. This paper describes early work undertaken to develop and validate a descriptive patient experience survey for AYA with cancer that encompasses both their cancer experience and age-related issues. We aimed to develop, with young people, an experience survey meaningful and relevant to AYA to be used in a longitudinal cohort study (BRIGHTLIGHT), ensuring high levels of acceptability to maximise study retention. Methods: A three-stage approach was employed: Stage 1 involved developing a conceptual framework, conducting literature/Internet searches and establishing content validity of the survey; Stage 2 confirmed the acceptability of methods of administration and consisted of four focus groups involving 11 young people (14-25 years), three parents and two siblings; and Stage 3 established survey comprehension through telephone-administered cognitive interviews with a convenience sample of 23 young people aged 14-24 years. Result: Stage 1: Two-hundred and thirty eight questions were developed from qualitative reports of young people's cancer and treatment-related experience. Stage 2: The focus groups identified three core themes: (i) issues directly affecting young people, e.g. impact of treatment-related fatigue on ability to complete survey; (ii) issues relevant to the actual survey, e.g. ability to answer questions anonymously; (iii) administration issues, e.g. confusing format in some supporting documents. Stage 3: Cognitive interviews indicated high levels of comprehension requiring minor survey amendments. Conclusion: Collaborating with young people with cancer has enabled a survey of to be developed that is both meaningful to young people but also examines patient experience and outcomes associated with specialist cancer care. Engagement of young people throughout the survey development has ensured the content appropriately reflects their experience and is easily understood. The BRIGHTLIGHT survey was developed for a specific research project but has the potential to be used as a TYA cancer survey to assess patient experience and the care they receive. [ABSTRACT FROM AUTHOR]