Rees, Jonathan

Aldridge, Roger Benjamin Lochore

Wellcome Trust

2018-06-05T10:37:25Z

2018-06-05T10:37:25Z

2018-06-30

Cutaneous malignancies represent a quarter of all new cancer diagnoses in the UK. The key to reducing the tumours’ associated mortality and morbidity is early diagnosis and treatment. Prompt diagnosis remains predominately a clinical skill, but relatively little investigation of the cognitive psychology underpinning expertise in this domain has been undertaken. This thesis aims to improve understanding of these processes and investigate how lesional diagnostic expertise might be enhanced. A large database of diagnostically tagged images was captured specifically for this project. A series of separate studies were undertaken to give insight into how lesional diagnosis occurs and how it can be improved. The studies highlighted that non-analytical pattern recognition (NAPR) is likely to predominate in distinguishing malignant and non-malignant skin lesions and that the widely-promoted rules advocating analytical pattern recognition (APR) are not effective for discriminating melanoma from benign pigmented lesions. The keystone to promoting the development of NAPR and thus diagnostic expertise would seem to be increasing a novice’s personal library of examples with relevant feedback. Studies demonstrated that current undergraduate exposure was variable but universally sparse, so simulation by way of diagnostically tagged images was developed which showed accuracy could be improved by increased exposure. This improvement occurred in both a content specific and dose responsive manner. These studies also highlighted that the learning curves for skin lesions are not uniform. Further studies demonstrated that the choice of images had implications on the development of diagnostic expertise; suggesting it was important that these images represent clinical practice rather than “classic” examples traditionally advocated for teaching purposes. In addition, studies highlighted the potential benefit of the 3D models developed during this project. Building on the idea that a personal catalogue of relevant referent images was crucial to enhanced diagnostic accuracy, prototype software was developed to exteriorise the experts’ library of examples; in the tests described novices utilising the software delivered superior accuracy than medical students on the completion of their undergraduate teaching. In summation, the work described shows that by utilising dermato-informatic approaches lesional diagnostic competence can be improved significantly.

http://hdl.handle.net/1842/31068

en

The University of Edinburgh

Aldridge RB, Naysmith L, Ooi ET, Murray CS, Rees JL. The importance of a full clinical examination: assessment of index lesions referred to a skin cancer clinic without a total body skin examination would miss one in three melanomas. Acta Derm Venereol. 2013; 93(6): 689- 92. PMID 23695107.

Ballerini L, Fisher RB, Aldridge B, Rees J, 2012. A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: ME Celebi, G Schaefer (Eds.) Color Medical Image Analysis, Springer (London) pp. 63-86. ISBN 9789400753884.

Aldridge RB, Maxwell SS, Rees JL. Dermatology undergraduate skin cancer training: A disconnect between recommendations, clinical exposure and competence. BMC Medical Education 2012; 12:27. PMID: 22569037.

Ballerini L, Fisher RB, Aldridge B, Rees J., 2012. Non-Melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. In: A Frangi, A Santos (Eds.) Proc. International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, IEEE pp. 358 to 361. ISBN: 978145771857.

Aldridge RB, Glodzik D, Ballerini L, Fisher RB, Rees JL. Utility of non-rule-based visual matching as a strategy to allow novices to achieve skin lesion diagnosis. Acta Derm Venereol. 2011; 91(3): 279-83. PMID: 21461552

Aldridge RB, Zanotto M, Ballerini L, Fisher RB, Rees JL. Novice identification of melanoma: not quite as straightforward as the ABCDs. Acta Derm Venereol. 2011; 91(2): 125-30. PMID: 21311845.

Li X, Aldridge B, Rees J, Fisher R., 2011. Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to with application to skin lesion segmentation. In: S Wright, X Pan, M Liebling (Eds.) Proc. International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, IEEE pp. 1438 to 1441. ISBN: 9781424441273.

Zanotto M, Ballerini L, Fisher RB, Aldridge B, Rees J., 2011. Visual Cues Do Not Improve Lesion ABC(D) Grading. In: DJ Manning, CK Abbey (Eds.) Proc. Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, SPIE vol. 7966. Bellingham: SPIE. pp. 79660U-1 to 79660U-10. ISBN: 9780819485083.

Li X, Aldridge B, Rees J, Fisher R., 2010. Estimating the ground truth from multiple individual segmentations with application to skin lesion segmentation. In: AH Bhalerao, NM Rajpoot (Eds.) Proc. Medical Image Understanding and Analysis (MIUA), BMVA Press. pp. 101 to 106. ISBN: 9780956615008.

Aldridge RB, Li X, Ballerini L, Fisher RB, Rees JL. Teaching dermatology using 3- dimensional virtual reality. Arch Dermatol. 2010; 146(10):1184-5; PMID: 20956667.

Laskaris N, Ballerini L, Fisher RB, Aldridge B, Rees J., 2010. Fuzzy description of skin lesions. In: DJ Manning, CK Abbey (Eds.) Proc. Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment, SPIE vol. 7627. Bellingham: SPIE. pp 762717-1 to 762717-10. ISBN: 9780819480286.

Ballerini L, Li X, Fisher RB, Aldridge B, Rees J., 2010. Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis. In: C di Chio, S Cagnoni, C Cotta, M Ebner, A Ekart, AI Esparcia-Alcazar, CK Gog, JJ Merelo, F Neri, M Preuss, J Togelius, GN Yannakakis (Eds.) Proc. Applications of Evolutionary Computation, LNCS vol. 6024. Berlin: Springer. pp 312 to 319. ISBN: 9783642122385.

Li X, Aldridge B, Ballerini L, Fisher B, Rees J., 2009. Depth data improves skin lesion segmentation. In: GZ Yang, D Hawkes, D Rueckert, A Noble, C Taylor (Eds.) Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS vol. 5762. Berlin: Springer. pp 1100 to1107. ISBN: 9783642042706. PMID: 20426221

skin cancer

early detection

skin lesions

diagnosis

imaging techniques

dermato-informatics

Dermato-informatic approaches to understanding and improving lesional diagnostic expertise in cutaneous oncology

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy