| 1. |
- Kvaerner, A. S., et al.
(författare)
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The CRCbiome study: a large prospective cohort study examining the role of lifestyle and the gut microbiome in colorectal cancer screening participants
- 2021
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Ingår i: Bmc Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Colorectal cancer (CRC) screening reduces CRC incidence and mortality. However, current screening methods are either hampered by invasiveness or suboptimal performance, limiting their effectiveness as primary screening methods. To aid in the development of a non-invasive screening test with improved sensitivity and specificity, we have initiated a prospective biomarker study (CRCbiome), nested within a large randomized CRC screening trial in Norway. We aim to develop a microbiome-based classification algorithm to identify advanced colorectal lesions in screening participants testing positive for an immunochemical fecal occult blood test (FIT). We will also examine interactions with host factors, diet, lifestyle and prescription drugs. The prospective nature of the study also enables the analysis of changes in the gut microbiome following the removal of precancerous lesions. Methods: The CRCbiome study recruits participants enrolled in the Bowel Cancer Screening in Norway (BCSN) study, a randomized trial initiated in 2012 comparing once-only sigmoidoscopy to repeated biennial FIT, where women and men aged 50-74 years at study entry are invited to participate. Since 2017, participants randomized to FIT screening with a positive test result have been invited to join the CRCbiome study. Self-reported diet, lifestyle and demographic data are collected prior to colonoscopy after the positive FIT-test (baseline). Screening data, including colonoscopy findings are obtained from the BCSN database. Fecal samples for gut microbiome analyses are collected both before and 2 and 12 months after colonoscopy. Samples are analyzed using metagenome sequencing, with taxonomy profiles, and gene and pathway content as primary measures. CRCbiome data will also be linked to national registries to obtain information on prescription histories and cancer relevant outcomes occurring during the 10 year follow-up period. Discussion: The CRCbiome study will increase our understanding of how the gut microbiome, in combination with lifestyle and environmental factors, influences the early stages of colorectal carcinogenesis. This knowledge will be crucial to develop microbiome-based screening tools for CRC. By evaluating biomarker performance in a screening setting, using samples from the target population, the generalizability of the findings to future screening cohorts is likely to be high.
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| 2. |
- Ali, Sharib, et al.
(författare)
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A multi-centre polyp detection and segmentation dataset for generalisability assessment.
- 2023
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Ingår i: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.
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| 3. |
- Ali, Sharib, et al.
(författare)
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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
- 2024
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Ingår i: SCIENTIFIC REPORTS. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
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| 4. |
- Botteri, Edoardo, et al.
(författare)
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Characteristics of non-participants in a randomized colorectal cancer screening trial comparing sigmoidoscopy and faecal immunochemical testing.
- 2022
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Ingår i: International journal of cancer. - : Wiley. - 1097-0215 .- 0020-7136. ; 151:3, s. 361-371
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Tidskriftsartikel (refereegranskat)abstract
- Public health systems should guarantee universal access to health care services, including cancer screening. We assessed whether certain population subgroups were underrepresented among participants in colorectal cancer screening with sigmoidoscopy and faecal immunochemical testing (FIT). Between 2012 and 2019, about 140,000 individuals aged 50-74years were randomly invited to once-only sigmoidoscopy or first round of FIT screening. This study included 46,919 individuals invited to sigmoidoscopy and 70,019 to FIT between 2012 and 2017. We used logistic regression models to evaluate if demographic and socioeconomic factors and use of certain drugs were associated with participation. 24,159 (51.5%) individuals attended sigmoidoscopy and 40,931 (58.5%) FIT screening. Male gender, young age, low education and income, being retired or unemployed, living alone, being an immigrant, long driving time to screening centre, and use of antidiabetic and psychotropic drugs were associated with low participation in both screening groups. Many of these factors also predicted low acceptance of colonoscopy after positive FIT. While male gender, young age and living alone were more strongly associated with non-participation in FIT than sigmoidoscopy, low education and income, being retired or immigrant and long driving time were more strongly associated with non-participation in sigmoidoscopy than FIT. In conclusion, participation was lower in sigmoidoscopy than FIT. Predictors of non-participation were similar between arms. However, low socioeconomic status, being an immigrant and long driving time affected participation more in sigmoidoscopy screening, suggesting that FIT may guarantee more equal access to screening services than sigmoidoscopy.
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| 5. |
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| 6. |
- Hicks, Steven, et al.
(författare)
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Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
- 2024
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Ingår i: PLOS ONE. - 1932-6203. ; 19:5
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations. This paper presents a study asking medical professionals about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that physicians see value in the explanations. Based on the feedback collected in our study, future explanations of medical deep neural networks can be tailored to the needs and expectations of doctors. Hopefully, this will contribute to solving the issue of black box medical systems and lead to successful implementation of this powerful technology in the clinic.
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| 7. |
- Jha, Debesh, et al.
(författare)
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A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
- 2021
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Ingår i: Medical Image Analysis. - 1361-8415 .- 1361-8423. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
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| 8. |
- Jha, D., et al.
(författare)
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A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
- 2021
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Ingår i: IEEE Journal of Biomedical and Health Informatics. - 2168-2194 .- 2168-2208. ; 25:6, s. 2029-2040
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Tidskriftsartikel (refereegranskat)abstract
- Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using CRF and TTA. We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF and TTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model's performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist, 196 sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.
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| 9. |
- Kirkøen, Benedicte, et al.
(författare)
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Type and Severity of Mental Illness and Participation in Colorectal Cancer Screening.
- 2023
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Ingår i: American journal of preventive medicine. - : Elsevier BV. - 1873-2607 .- 0749-3797. ; 64:1, s. 76-85
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Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of colorectal cancer screening programs depends on the participation rate. This study examined the association between type and severity of mental illness and colorectal cancer screening participation.Between 2012 and 2017, a total of 46,919 individuals were invited to sigmoidoscopy screening in Norway, and 70,019 were invited to fecal immunochemical testing. In 2022, logistic regression was used to evaluate the association between the use of antipsychotics, anxiolytics, hypnotics, and antidepressants in the year preceding the screening invitation and screening participation, adjusted for demographic and socioeconomic factors. Defined daily doses of individual drugs were used to assess dose‒response relationships.Overall, 19.2% (24.8% of women, 13.4% of men) of all invitees used at least 1 psychotropic medication. Nonparticipation in the 2 arms combined was associated with the use of anxiolytics (60.7% in users vs 43.2% in nonusers; OR=1.53; 95% CI=1.45, 1.62) and antipsychotics (64.3% vs 43.8%; OR=1.41; 95% CI=1.30, 1.53) and increased with higher doses for both drugs. Hypnotics and antidepressants were only weakly associated with nonparticipation in higher doses. Participation rates were 57.3%, 52.3%, 42.9%, and 35.4% in those prescribed 0, 1, 2, and 3-4 classes of psychotropic medications, respectively. The associations between the use of psychotropic medications and nonparticipation were similar for the 2 screening tests.These findings show significant disparities in colorectal cancer screening participation for individuals with mental illness, independent of the screening method. Moreover, screening participation varied depending on the type and severity of mental illness. Targeted interventions are warranted to ensure that people with mental illness are supported to access the benefits of colorectal cancer screening.
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| 10. |
- Leenhardt, R., et al.
(författare)
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Key research questions for implementation of artificial intelligence in capsule endoscopy
- 2022
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Ingår i: Therapeutic Advances in Gastroenterology. - : SAGE Publications. - 1756-283X .- 1756-2848. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Results: Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). Conclusion: In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
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