Cancer Diagnostic Process Research Articles

Global Research of the Use of Contrast-Enhanced Ultrasound in Cancer Diagnostic Process: A Bibliometric Analysis and Future Perspective

Pendahuluan: Contrast-enhanced ultrasound (CEUS) dapat digunakan untuk membedakan beberapa jenis tumor ganas. Namun, analisis bibliometrik yang merangkum tren penelitian global dengan topik CEUS untuk mendiagnosis kanker masih terbatas. Penelitian ini bertujuan untuk mengamati tren perkembangan CEUS dalam mendiagnosis kanker dan mengusulkan penelitian di masa depan. Metode: Analisis bibliometrik dilakukan terhadap data publikasi yang diambil dari database Scopus dengan pencarian kata kunci tertentu. Pencarian data diselesaikan dalam waktu satu hari (1 Juni 2024) untuk menghindari kemungkinan bias akibat mekanisme pembaruan database Scopus secara berkala. Data dibatasi untuk publikasi berupa artikel yang diterbitkan sejak awal publikasi hingga tahun 2023. Data-data berupa profil bibliografi, tren tahunan, dan hotspot penelitian dianalisis dan divisualisasikan menggunakan VOSviewer. Hasil: Sejak tahun 1994, terdapat peningkatan keluaran publikasi tahunan dari 2605 dokumen yang teridentifikasi dengan tingkat pertumbuhan rata-rata sebesar 2,35%. Tiga negara yang paling produktif terhadap penelitian topik ini yaitu Tiongkok (50,67%), Jepang (10,17%), dan Amerika Serikat (9,21%). “Ultrasound in Medicine and Biology” menerbitkan artikel terbanyak yang berkaitan dengan topik ini. Penelitian di bidang kedokteran (medicine) paling banyak diminati, diikuti penelitian di bidang biokimia, genetika, dan biologi molekuler (biochemistry, genetics and molecular biology). Sebanyak 4032 kata kunci diklasifikasikan ke dalam delapan kelompok termasuk contrast-enhanced ultrasound, breast cancer, hepatocellular carcinoma, diagnosis, liver, contrast media, metastasis, dan sonazoid. Kesimpulan: Studi bibliometrik ini menunjukkan tren global CEUS dalam bidang diagnosis keganasan. Selain biayanya yang rendah dan keunggulan tanpa radiasi, CEUS merupakan modalitas diagnostik yang menjanjikan untuk membedakan lesi ganas. Penelitian pada topik ini masih dapat dikembangkan pada berbagai lokasi dan jenis keganasan.

Abstract PO2-13-12: Serum Thioredoxin 1 Enhances Breast Cancer Detection in Mammography and Breast Ultrasound, Including Dense Breasts

Abstract Background: Breast cancer (BC), when detected in its early stages, generally carries a more favorable prognosis than other types of cancer. Currently, the screening and early detection of breast cancer rely predominantly on image-oriented diagnostic methods. However, these methods inherently fall short in capturing the dynamic nature of cancer cells. Mammography has known limitations in clinical sensitivity, particularly in cases with high breast density. While breast ultrasound can mitigate some of these limitations, there remain unmet needs in this field. Previously, we reported the promising clinical performance (Sensitivity: 96.43%, Specificity: 97.32%) of serum thioredoxin 1 (Trx1) as a potential novel means for detecting breast cancer. In this study, we aim to evaluate the extent to which serum Trx1 can enhance the accuracy of breast cancer diagnosis, including in dense breasts, within the imaging-based diagnostic system powered by mammography and ultrasonography. Methods: We have generated monoclonal antibodies against Trx1 and developed an ELISA kit (DxMe® BC Kit, E&S Healthcare, Korea) that quantifies Trx1 in serum. A total of 308 biopsy-confirmed breast cancer patients, who had undergone mammography prior to their final diagnosis, were recruited. The clinical information of these 308 patients was retrospectively examined and collected. The data from 254 individuals, who also had results from breast ultrasound, and 261 individuals, who had records of breast density, were analyzed collectively. Freshly collected blood was prepared as previously indicated, and then tested with the kit to determine the level of Trx1. All serum samples were measured twice, and statistical analyses were conducted using ROC analysis, one-way ANOVA, and unpaired t-tests. Results: Clinical sensitivity for BC detection was analyzed using the Trx1 levels of the 254 subjects who underwent both mammography and breast ultrasound. The respective sensitivities of mammography, breast ultrasound, and the DxMe® BC Kit were 72.83%, 92.91%, and 96.46%. With combined analysis of mammography, breast ultrasound, and Trx1, the sensitivity increased to 98.43% for mammography + breast ultrasound, and 98.82% for mammography + Trx1. When breast ultrasound + Trx1 was considered, the sensitivity rose to 99.61%. Likewise, the sensitivity of mammography + breast ultrasound + Trx1 was also measured at 99.61%. Among the 261 breast cancer patients with available information about breast density, 186 patients (71.3%) had dense breasts. The clinical sensitivity of mammography and Trx1 was specifically analyzed for these dense breast cases. Mammography demonstrated a sensitivity of 64.3%, while the DxMe® BC kit exhibited a sensitivity of 97.2%. When mammography and Trx1 were analyzed in parallel, sensitivity increased to 99.7%. Conclusion: This study demonstrated that the DxMe® BC kit, which quantifies serum Trx1 levels, can enhance the accuracy of conventional image-based diagnostics for breast cancer screening and early detection by more than 25%. This improvement in sensitivity was observed when mammography and breast ultrasound were each used in conjunction with Trx1, with no significant difference noted between these two combinations. Notably, in the case of dense breasts, the Trx1 test improved sensitivity by more than 30% compared to mammography. Given that sensitivity increases when mammography and Trx1 tests are used together, it showed that incorporating the Trx1 test alongside traditional mammography could significantly enhance diagnostic performance in dense breasts up to 35%. This approach has the potential to streamline the breast cancer diagnostic process, reduce time and cost, and improve the reliability of current breast cancer screening and early detection methods, regardless of breast density. Citation Format: Young Kim, Hyemi Ko, Yeri Lee, Keunkoo Shin, Jongam Song, Jeryoung Kim, Kyounghoon Suh, Jinsun Lee. Serum Thioredoxin 1 Enhances Breast Cancer Detection in Mammography and Breast Ultrasound, Including Dense Breasts [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-12.

The importance of rhythms for maintaining consent in diagnostic encounters to detect cervical cancer.

Diagnostic encounters can be seen as complex socio-material processes. Drawing on the new materialist ideas of Barad, we studied how an innovative technology became part of the intra-actions between different human and non-human materialities in a cervical cancer diagnostic process. While researching the development of a technology intended to improve cervical cancer detection, we carried out a series of observations of diagnostic encounters involving clinicians, patients and the device in a hospital. The intra-actions between the different materialities had rhythmic properties, repeated activities and timings that varied in intensity, for example, movements, exchanged looks, and talk that helped co-produce the diagnosis and maintain consent. Sadly, the device interfered with the rhythms, undermining the clinicians' desire to adopt it, despite it being more accurate at diagnosing ill health than previous assistive technologies. Studying rhythms as part of diagnostic encounters could help with the design and subsequent integration of novel technologies in healthcare, because they encompass relationships created by human and non-human materialities. Importantly, highlighting the role of rhythms contributes another way diagnostic encounters are co-produced between clinicians and patients, and how they can be disrupted, improving the understanding of how consent is maintained or lost.

Psychiatric disorders and the cancer diagnostic process in general practice: a combined questionnaire and register study exploring the patients’ experiences in Denmark

Objective Patients with psychiatric disorders are at risk of experiencing suboptimal cancer diagnostics and treatment. This study investigates how this patient group perceives the cancer diagnostic process in general practice. Design Cross-sectional study using questionnaire and register data. Setting General practice in Denmark. Subjects Patients diagnosed with cancer in late 2016 completed a questionnaire about their experiences with their general practitioner (GP) in the cancer diagnostic process (n = 3411). Information on pre-existing psychiatric disorders was obtained from register data on psychiatric hospital contacts and primary care treated psychiatric disorders through psychotropic medications. Logistic regression was used to analyse the association between psychiatric disorders and the patients’ experiences. Main outcome measures Patients’ experiences, including cancer worry, feeling being taken seriously, and the perceived time between booking an appointment and the first GP consultation. Box 1. Included survey items on the patients’ experiences Results A total of 13% of patients had an indication of a psychiatric disorder. This group more often perceived the time interval as too short between the first booking of a consultation and the first GP consultation. Patients with primary care treated psychiatric disorders were more likely to worry about cancer at the first presentation and to share this concern with their GP compared with patients without psychiatric disorders. We observed no statistically significant association between patients with psychiatric disorders and perceiving the waiting time to referral from general practice, being taken seriously, trust in the GP’s abilities, and the patients’ knowledge of the process following the GP referral. Conclusion The patients’ experiences with the cancer diagnostic process in general practice did not vary largely between patients with and without psychiatric disorders. Worrying about cancer may be a particular concern for patients with primary care treated psychiatric disorders.

Time duration and health care resource use during cancer diagnoses in the United States: A large claims database analysis.

BACKGROUND: Cancer diagnostic pathways are highly variable and not clearly established in the United States, which can lead to a diagnosis process that takes more time and exposes patients to invasive or unnecessary procedures, delays in treatment, worsening patient outcomes, and elevated health care resource utilization (HRU) and health care system costs. OBJECTIVE: To investigate current trends in time to diagnosis and diagnostic-related HRU preceding the patient's cancer diagnosis across all cancer types in the United States. METHODS: A retrospective claims analysis was conducted on patients newly diagnosed with cancer identified from 2018-2019 using Optum's de-identified Clinformatics Data Mart database, which includes Medicare Advantage and commercially insured members. Patients were identified using International Classification of Diseases, Tenth Revision codes and were required to have at least 2 outpatient visits at least 30 days apart or at least 1 inpatient cancer visit without prior cancer claims. The first diagnostic test was identified based on an algorithm of a 60-day gap between diagnostic tests prior to diagnosis. The index date was defined as the first diagnostic test date or an office visit less than 4 weeks prior to the first diagnostic test date. Patient characteristics, time to diagnosis, and HRU were descriptively analyzed for all patients and by cancer type. RESULTS: Among the 458,818 patients newly diagnosed with cancer included in this analysis, the mean age was 70.6 years, approximately half were female, and most were White people (65.0%) with Medicare Advantage coverage (74.0%). Patients with cancer had an overall mean (SD) time to diagnosis of 156.2 (164.9) days and 15.4% of patients waited longer than 180 days before a cancer diagnosis. High heterogeneity among cancer types was observed, with a mean time to diagnosis ranging from 121.6 days (bladder cancer) to 229.0 days (multiple myeloma). Imaging resource use during the diagnostic pathway was high for radiology (60.7%), computerized tomography (50.8%), magnetic resonance imaging (48.6%), and ultrasound (42.6%). A total of 69.3% of patients had endoscopy without biopsy, 36.5% had endoscopy with biopsy, 62.5% had other biopsies, and most patients did general urine and serum tests (91.3%) and nongenetic cancer-specific laboratory tests (84.3%). Resource use was highly varied by cancer type but tended to increase with a longer time to diagnosis. CONCLUSIONS: The proportion of patients experiencing a diagnostic process of longer than 180 days is clinically and economically meaningful. Diagnostic-related HRU was significant and highly variable, highlighting the inefficiencies in the cancer diagnostic process in the United States and the need for policies, guidelines, or medical interventions to streamline cancer diagnostic pathways to optimize patient outcomes and reduce health care system burden. DISCLOSURES: Dr Cong is an employee of Grail, LLC, which supported this study. Drs Gitlin and McGarvey are employees of BluePath Solutions, and Ms Shivaprakash was an employee of BluePath Solutions, which received financial support from Grail, LLC, for study-related research activities. This study was sponsored by Grail, LLC, a subsidiary of Illumina Inc. currently held separate from Illumina Inc. under the terms of the Interim Measures Order of the European Commission dated October 29, 2021. The sponsor had no role in the collection, management, and analysis of the data. The sponsor contributed to study design and data interpretation.

Developing electronic clinical quality measures to assess the cancer diagnostic process.

Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance. We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer. The 2 eCQMs quantified rates of red-flag follow-up in CRC and lung cancer using electronic health record data repositories at 2 large healthcare systems. Each measure used clinical data to identify abnormal results, evidence of appropriate follow-up, and exclusions that signified follow-up was unnecessary. Clinicians reviewed 100 positive and 20 negative randomly selected records for each eCQM at each site to validate accuracy and categorized missed opportunities related to system, provider, or patient factors. We implemented the CRC eCQM at both sites, while the lung cancer eCQM was only implemented at the VA due to lack of structured data indicating level of cancer suspicion on most chest imaging results at Geisinger. For the CRC eCQM, the rate of appropriate follow-up was 36.0% (26 746/74 314 patients) in the VA after removing clinical exclusions and 41.1% at Geisinger (1009/2461 patients; P < .001). Similarly, the rate of appropriate evaluation for lung cancer in the VA was 61.5% (25 166/40 924 patients). Reviewers most frequently attributed missed opportunities at both sites to provider factors (84 of 157). We implemented 2 eCQMs to evaluate the diagnostic process in cancer at 2 large health systems. Health care organizations can use these eCQMs to monitor diagnostic performance related to cancer.

Using care pathways for cancer diagnosis in primary care: a qualitative study to understand family physicians' mental models.

Care pathways are tools that can help family physicians navigate the complexities of the cancer diagnostic process. Our objective was to examine the mental models associated with using care pathways for cancer diagnosis of a group of family physicians in Alberta. We conducted a qualitative study using cognitive task analysis, with interviews in the primary care setting between February and March 2021. Family physicians whose practices were not heavily oriented toward patients with cancer and who did not work closely with specialized cancer clinics were recruited with the support of the Alberta Medical Association and leveraging our familiarity with Alberta's Primary Care Networks. We conducted simulation exercise interviews with 3 pathway examples over Zoom, and we analyzed data using both macrocognition theory and thematic analysis. Eight family physicians participated. Macrocognitive functions (and subthemes) related to mental models were sense-making and learning (confirmation and validation, guidance and support, and sense-giving to patients), care coordination and diagnostic decision-making (shared understanding). Themes related to the use of the pathways were limited use in diagnosis decisions, use in guiding and supporting referral, only relevant and easy-to-process information, and easily accessible. Our findings suggest the importance of designing pathways intentionally for streamlined integration into family physicians' practices, highlighting the need for co-design approaches. Pathways were identified as a tool that, used in combination with other tools, may help gather information and support cancer diagnosis decisions, with the goals of improving patient outcomes and care experience.

Expression of RBMS3 in Breast Cancer Progression.

The aim of the study was to evaluate the localization and intensity of RNA-binding motif single-stranded-interacting protein 3 (RBMS3) expression in clinical material using immunohistochemical (IHC) reactions in cases of ductal breast cancer (in vivo), and to determine the level of RBMS3 expression at both the protein and mRNA levels in breast cancer cell lines (in vitro). Moreover, the data obtained in the in vivo and in vitro studies were correlated with the clinicopathological profiles of the patients. Material for the IHC studies comprised 490 invasive ductal carcinoma (IDC) cases and 26 mastopathy tissues. Western blot and RT-qPCR were performed on four breast cancer cell lines (MCF-7, BT-474, SK-BR-3 and MDA-MB-231) and the HME1-hTERT (Me16C) normal immortalized breast epithelial cell line (control). The Kaplan-Meier plotter tool was employed to analyze the predictive value of overall survival of RBMS3 expression at the mRNA level. Cytoplasmatic RBMS3 IHC expression was observed in breast cancer cells and stromal cells. The statistical analysis revealed a significantly decreased RBMS3 expression in the cancer specimens when compared with the mastopathy tissues (p < 0.001). An increased expression of RBMS3 was corelated with HER2(+) cancer specimens (p < 0.05) and ER(-) cancer specimens (p < 0.05). In addition, a statistically significant higher expression of RBMS3 was observed in cancer stromal cells in comparison to the control and cancer cells (p < 0.0001). The statistical analysis demonstrated a significantly higher expression of RBMS3 mRNA in the SK-BR-3 cell line compared with all other cell lines (p < 0.05). A positive correlation was revealed between the expression of RBMS3, at both the mRNA and protein levels, and longer overall survival. The differences in the expression of RBMS3 in cancer cells (both in vivo and in vitro) and the stroma of breast cancer with regard to the molecular status of the tumor may indicate that RBMS3 could be a potential novel target for the development of personalized methods of treatment. RBMS3 can be an indicator of longer overall survival for potential use in breast cancer diagnostic process.

Factors Associated with the Breast Cancer Diagnostic Interval across Five Canadian Provinces: A CanIMPACT Retrospective Cohort Study.

The cancer diagnostic process can be protracted, and it is a time of great anxiety for patients. The objective of this study was to examine inter- and intra-provincial variation in diagnostic intervals and explore factors related to the variation. This was a multi-province retrospective cohort study using linked administrative health databases. All females with a diagnosis of histologically confirmed invasive breast cancer in British Columbia (2007-2010), Manitoba (2007-2011), Ontario (2007-2010), Nova Scotia (2007-2012), and Alberta (2004-2010) were included. The start of the diagnostic interval was determined using algorithms specific to whether the patient's cancer was detected through screening. We used multivariable quantile regression analyses to assess the association between demographic, clinical and healthcare utilization factors with the diagnostic interval outcome. We found significant inter- and intra-provincial variation in the breast cancer diagnostic interval and by screen-detection status; patients who presented symptomatically had longer intervals than screen-detected patients. Interprovincial diagnostic interval variation was 17 and 16 days for screen- and symptom-detected patients, respectively, at the median, and 14 and 41 days, respectively, at the 90th percentile. There was an association of longer diagnostic intervals with increasing comorbid disease in all provinces in non-screen-detected patients but not screen-detected. Longer intervals were observed across most provinces in screen-detected patients living in rural areas. Having a regular primary care provider was not associated with a shorter diagnostic interval. Our results highlight important findings regarding the length of the breast cancer diagnostic interval, its variation within and across provinces, and its association with comorbid disease and rurality. We conclude that diagnostic processes can be context specific, and more attention should be paid to developing tailored processes so that equitable access to a timely diagnosis can be achieved.

Clinical diagnosis of breast cancer samples using artificial immune systems with negative selection / Diagnóstico clínico de amostras de cancro da mama utilizando sistemas imunitários artificiais com selecção negativa

This paper uses an artificial immune system applied for diagnosing breast cancer samples. Taking as basis an immunological process, the negative selection algorithm was used to discriminate the samples, attaining a classification for benign or malignant cases. The main application of the method is to assist professionals in the breast cancer diagnostic process, thereby providing decision-making agility, efficient treatment planning, reliability and the necessary intervention to save lives. To evaluate this method, the wisconsin breast cancer diagnosis database was used. This is an actual breast cancer database. The results obtained using the method, when compared with the specialized literature, show accuracy, robustness and efficiency in the breast cancer diagnostic process.

Understanding factors associated with hospital admission for colon cancer diagnosis and staging work up.

17 Background: Cancer diagnosis, staging work up and treatment need to be done in a timely manner but are rarely emergencies. Cancer work up during a hospital admission is costly and can take away resources that are needed for more urgent patients. We aim to understand factors related to cancer work up in the hospital inpatient setting. Methods: Patients with a new diagnosis of colon cancer at a single academic institution in 2018 were identified using cancer registry. Patients were excluded if they had cancer other than carcinoma or if data on more than half of their work up was not available for review. A chart review was performed to obtain factors including their demographics, details on their cancer presentation, diagnostic and staging work up and treatment. Patients were considered to have had inpatient work up (Inpt_WU) if 50% or more of their cancer related diagnostic, staging and pre-treatment work up was conducted during a hospital admission, and included imaging, bloodwork, evaluation by consultants. Inpt_WU and non-Inpt_WU patients were compared. Logistic regression model was used calculate odds ratio (OR) and 95% confidence intervals (CI) to identify associations with Inpt_WU. Results: Data on 121 newly diagnosed colorectal cancer patients were analyzed. Inpt_WU occurred in 63 (52%) of the cohort. Inpt_WU and non-Inpt_WU patients required the same median number of work-up events [6, interquartile range (IQR) 5, 6]. Inpt_WU patients were more likely to initially present to the emergency department (ED: 91% vs 16%, p&lt;0.001), have Medicaid insurance (40% vs 17%, p=0.018), be functionally dependent (30% vs 16%, p=0.091), have more defined symptoms (75% vs 45% p=0.001), and have greater cancer severity (metastatic disease 33% vs 17%, p=0.004). The median time between presentation and treatment initiation was markedly less for Inpt_WU compared to non-Inpt_WU [11 days (IQR 5, 24) vs 48 (IQR 37, 81), p&lt;0.001]. The logistic model showed that ED presentation had over 40-fold higher odds of Inpt_WU (OR 41.2, 95% CI 12.1-177.4) after adjusting for comorbidities, insurance, functional status, symptoms, and cancer severity. Conclusions: The cancer diagnostic and pre-treatment work-up process was markedly expedited when done during hospital admission. Most cancers presenting to the ED as the initial location of presentation resulted in admission and cancer work up in the hospital. Further studies are needed to explore whether all of these patients necessitate inpatient admission, and whether healthcare systems can be modified to mimic the efficiency of in-hospital workup.

Cancer diagnosis and treatment in the COVID‐19 era

Screening and early symptom-based detection of cancer offer the prospect of diagnosing cancers at a stage where treatment can lead to either prolonged survival or cure. While screening programmes for breast, colorectal and cervical cancer are established in most developed countries, other programmes such as lung cancer screening are also beginning to emerge. We frequently publish papers in EJCC on uptake in cancer screening programmes—if such programmes cannot achieve acceptable levels of uptake in the community, their population health benefits diminish significantly. We also regularly publish papers on promoting early diagnosis—for example through public awareness campaigns and strategies to improve recognition and referral of cancers in primary care. In this issue of the journal, Karakuş Selçuk and Yanikkerem (2019) describe a web-based intervention to improve participation in cervical cancer screening. Like many such interventions, they demonstrate a benefit in this mixed methods study. Cervical cancer is a good example of the ongoing effort that needs to be invested in cancer screening programmes. In recent years, there have been concerns for example in falling rates of cervical screening in the UK (Castanon & Sasieni, 2018). Increasingly, novel methods, such as those described by Karakus et al (2020), are required to maintain participation. This is particularly so, in the case of cervical screening, with the advent of HPV vaccination and testing, which has perceptions about the importance of cervical screening (Patel, Moss, & Sherman, 2018). Also in this issue, Murchie et al. (2020) report on an audit of cancer referrals from primary care in Scotland. This is clearly an important ongoing initiative to monitor such referrals and look for lessons about how primary care can more effectively recognise cancer symptoms and make appropriate referrals. The authors present data on key indicators including cancer referral rates and characteristics of cancers detected. There has, over the last decade, been a great deal of research which has shone a light on the cancer diagnostic process in primary care; we now know a great deal more about how symptoms are assessed and referrals made in primary care (Rubin et al., 2015). For example that cancers with vague symptoms typically have longer intervals (Lyratzopoulos, Neal, Barbiere, Rubin, & Abel, 2012). The data from the National Cancer Diagnosis Audit will be an important ongoing source of cancer intelligence in Scotland. It would be difficult, however, to talk about screening and early diagnosis without acknowledging the profound impact of the COVID-19 epidemic. In the UK and in many other countries, cancer screening has been put on hold—this is likely to have very significant downstream implications. Also, we know that cancer referrals for primary care have dropped very dramatically as both primary and secondary care reorient their focus towards the COVID-19 epidemic. Clearly, there are difficult decisions to be made over COVID-19 and ongoing cancer control efforts; it has transformed every aspect of cancer care and has impacted profoundly on the cancer workforce (Mayor, 2020). It has caused many health planners to think carefully about how they can manage cancer through an epidemic such as this; cancer treatments are profoundly impacted and we know that cancer patients, particularly those receiving chemotherapy, are especially vulnerable to COVID-19—so difficult decisions are needing to be made about treatment decisions for many cancer patients (Al-Shamsi et al., 2020; Vrdoljak, Sullivan, & Lawler, 2020). Modelling studies suggest that there are likely to be excess deaths from cancer based on the current reconfiguration of health service activity; two UK studies which are yet to be published have provided estimates of excess cancer mortality and highlight the need for urgent attention from health planners and policy makers (Alvina et al, 2020; Sud et al., 2020). We urgently need a joined up dialogue about maintaining cancer diagnostic and treatment services whilst addressing the worst aspects of the COVID-19 epidemic. We are at the beginning of a rather steep learning curve, but there can be no doubt that research examining the impact on cancer outcomes is urgently needed if we are to limit the collateral damage from this epidemic. The journal is beginning to receive an increasing number of papers examining these issues, and we urge researchers in this field to keep sending in their submissions.

The convivial and the pastoral in patient-doctor relationships: a multi-country study of patient stories of care, choice and medical authority in cancer diagnostic processes.

Experiences of cancer diagnosis are changing in light of both the increasingly technological‐clinical diagnostic processes and the socio‐political context in which interpersonal relations take place. This has raised questions about how we might understand patient–doctor relationship marked by asymmetries of knowledge and social capital, but that emphasise patients’ empowered choices and individualised care. As part of an interview study of 155 participants with bowel or lung cancer across Denmark, England and Sweden, we explored participants’ stories of the decisions made during their cancer diagnostic process. By focusing on the intersections of care, choice and medical authority – a convivial pastoral dynamic – we provide a conceptual analysis of the normative ambivalences in people's stories of their cancer diagnosis. We found that participants drew from care, choice and medical authority to emphasise their relationality and interdependence with their doctors in their stories of their diagnosis. Importantly negotiations of an asymmetrical patient–doctor relationship were part of an on‐going realisation of the healthcare processes as a human endeavour. We were therefore able to draw attention to the limitations of dichotomising emancipatory‐empowerment discourses and argue for a theorisation of the patient–doctor relationship as a contextually bounded and relationally ambivalent humanity.

Factors associated with the breast cancer diagnostic interval across five Canadian provinces: a CanIMPACT study

IntroductionA long breast cancer diagnostic process can affect patient anxiety and survival. Variations in the length of the diagnostic interval for similar patient presentations can indicate health system inequities and/or inefficiencies.&#x0D; Objectives and ApproachWe describe the breast cancer diagnostic interval across Canada and factors associated with its length.&#x0D; We studied breast cancer patients diagnosed from 2004/7 to 2010/11/12 in the Canadian provinces: British Columbia, Alberta, Manitoba, Ontario, and Nova Scotia. Using administrative data, we created parallel population-based, provincial-level datasets and ran common analyses. The diagnostic interval was defined from the screening mammogram to the diagnosis for screen-led and from the first referral/test ordering date to the diagnosis for diagnostic-led patients. Stratified by these two diagnostic routes, we describe the variation in the interval across provinces and report on the province-specific associations between the diagnostic interval and: patient age, comorbid disease burden, socioeconomic status combined with rural residence, and continuity of primary care while controlling for cancer stage.&#x0D; ResultsThe median diagnostic interval varied by 6 days (29 to 35 days) across provinces. Screen-led patients were diagnosed more quickly (median 2-12 days quicker). The 90th percentile diagnostic interval was 84-126 days longer in diagnostic-led patients. In the diagnostic-led group, increasing comorbid burden was consistently associated with longer diagnostic intervals and being &gt;70 was associated with a shorter interval at the 90th percentile in Manitoba and Ontario. There was no evidence of a clear rural or low socioeconomic status effect and patients without a primary care physician had shorter intervals. In the screen-led group, patients age 40-49 and those in the medium or low income rural areas waited longer for a diagnosis.&#x0D; Conclusion/ImplicationsDiagnostic wait times differ across Canada and are variably associated with comorbidity, age, area-level socioeconomic status and rural residence. These results point to practice and system-level effects that warrant further study.

A multi-level thresholding method for breast thermograms analysis using Dragonfly algorithm

Breast cancer is one of the most common diseases and the second cause of death in women around the world. The presence of a cancerous tumor increase temperature in the region near to it, such heating is then transferred to the skin surface. In this sense, screening seeks to help in cancer diagnostic process before symptoms became evident in a person, different imaging techniques are employed for this purpose (Mammography, Ultrasonography, X-ray, Magnetic Resonance, etc). In the past decade, thermography has shown its major potential to early diagnosis of breast diseases. Thermographic images provide information related to vascular or physiological changes and have some advantages regarding other diagnostic methods; they are non-ionizing, non-invasive, passive, painless and real-time screening. On the other hand, thresholding has been widely used to solve several problems. It is regularly the first step in the process of image analysis that uses histograms to classify the pixels in the image. Segmentation of medical digital images has been stated as an important task for several medical applications. This paper proposes a segmentation technique for thermographic images that consider the spatial information of the pixel contained in the image. This approach employs a novel optimization technique called the Dragonfly Algorithm to compute the best thresholds that segment the image. The experimental results exhibit a well-performance of the proposal in comparison to the other methods over a set of randomly selected thermograms retrieved from the Database for Research Mastology with Infrared Image. The presented proposed approach could provide a highly reliable clinical decision support, which aims to help clinicians in performing a diagnosis using thermography images.

Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images

Background and objectiveThis paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process. MethodsThe proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring. ResultsExperimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods. ConclusionsThe proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.

Differences in the breast cancer diagnostic process across stage groups in Ontario, Canada

ABSTRACT&#x0D; ObjectivesEarly diagnosis leads to better cancer survival and short diagnostic intervals reduce patient anxiety. We are studying factors that prolong the breast cancer diagnostic process in Ontario, Canada. &#x0D; ApproachThis is a retrospective study of all patients diagnosed 2007-2011 (n=33,752). We linked data from Cancer Care Ontario and the Institute for Clinical Evaluative Sciences including: Ontario Cancer Registry, physician claims, ambulatory, ER visits, and hospital discharges. Detection method (screening versus symptomatic) was determined using Screening Program and claims data. The diagnostic interval is the time from first relevant health care encounter to the definitive diagnosis. Elements of the diagnostic interval include: use of imaging, biopsy, and the number of encounters and providers.&#x0D; ResultsOverall, 30.6% were screen-diagnosed and the median diagnostic interval was 40 days (IQR 21-80). The median interval was shorter in the screened group at 32 days versus symptomatic at 45 days. The diagnostic interval was longer for stage I patients at 47 days compared to stage II (37 days), stage III (33 days) or stage IV (22 days). Stage IV patients were less likely to be diagnosed via biopsy (44% vs 61%) and the symptomatic stage IV subgroup less likely to have breast imaging (61% vs 96%). 26% of stage IV patients saw 0 or 1 providers while 8% of stage I patients saw 6 or more. 19% of stage I patients had 10 or more encounters overall versus 15% and 28% had &gt;1 mammogram versus 14%. Effects are largely similar in screened and symptomatic groups.&#x0D; ConclusionShorter diagnostic intervals in stage IV are associated with a more direct diagnostic path. We will present results quantifying the number of days attributable to the diagnostic elements. Understanding the impact of elements of the diagnostic process provide targets for improvement of its length.

Kernel-based learning and feature selection analysis for cancer diagnosis

DNA microarray is a very active area of research in the molecular diagnosis of cancer. Microarray data are composed of many thousands of features and from tens to hundreds of instances, which make the analysis and diagnosis of cancer very complex. In this case, gene/feature selection becomes an elemental and essential task in data classification. In this paper, we propose a complete cancer diagnostic process through kernel-based learning and feature selection. First, support vector machines recursive feature elimination (SVM-RFE) is used to prefilter the genes. Second, the SVM-RFE is enhanced by using binary dragonfly (BDF), which is a recently developed metaheuristic that has never been benchmarked in the context of feature selection. The objective function is the average of classification accuracy rate generated by three kernel-based learning methods. We conducted a series of experiments on six microarray datasets often used in the literature. Experiment results demonstrate that this approach is efficient and provides a higher classification accuracy rate using a reduced number of genes.

677. Time to diagnosis before and after the implementation of breast cancer rapid management policy

Background: Delays at any stage of the breast cancer diagnostic process may result in high anxiety for the woman. Targets should be set at every stage where delay may arise. The aim of this study was to investigate the therapeutic delay in women with primarily operable breast cancer before and after the implementation of breast cancer rapid management policy (BCRMP) in our institution and compare it to the European Commission (EC) recommendations.

Intelligent System for Detection of Abnormalities in Human Cancerous Cells and Tissues

Due to the latest advances in the field of MML (Medical Machine Learning) a significant change has been witnessed and traditional diagnostic procedures have been converted into DSS (Decision Support Systems). Specially, classification problem of cancer discovery using DICOM (Digital Communication in Medicine) would assume to be one of the most important problems. For example differentiation between the cancerous behaviours of chromatin deviations and nucleus related changes in a finite set of nuclei may support the cytologist during the cancer diagnostic process. In-order to assist the doctors during the cancer diagnosis, this paper proposes a novel algorithm BCC (Bag_of_cancerous_cells) to select the most significant histopathological features from the well-differentiated thyroid cancers. Methodology of proposed system comprises upon three layers. In first layer data preparation have been done by using BMF (Bag of Malignant Features) where each nuclei is separated with its related micro-architectural components and behaviours. In second layer decision model has been constructed by using CNN (Convolutional Neural Network) classifier and to train the histopathological behaviours such like BCP (Bags of chromatin Paches) and BNP (Bags of Nuclei Patches). In final layer, performance evaluation is done. A total number of 4520 nuclei observations were trained to construct the decision models from which BCP (Bags of Chromatin Patches) consists upon the 2650 and BNP (Bags of Nuclei Patches) comprises upon 1870 instances. Best measured accuracy for BCP was recorded as 97.93% and BNP accuracy was measured as 97.86%.