Developing a national artificial intelligence-assisted skin cancer pathway

The AI Skin Cancer Consortium seeks to progress integration of artificial intelligence (AI) into the skin cancer referral pathway to promote rapid diagnosis of skin cancer and reassurance with benign lesions. In contrast to other fields of medicine, such as radiology, dermatology has lacked standardization in data acquisition, which is required for high-quality, reproducible and interoperable AI algorithm performance. Beginning in 2021, the consortium has made substantial progress towards defining a national pathway, technology architecture and data flows. We describe a standardized approach to acquisition of macroscopic and dermoscopic images, and technical and clinical metadata in a DICOM (Digital Imaging and Communications in Medicine) format, currently acquired in outpatient imaging centres and in general practice. In order to develop a transparent competitive environment for testing of this standardized National Health Service (NHS) pipeline, the consortium created a Small Business Research Initiative funded via a government agency. Three companies were awarded funding for this two-phase programme, from 15 applicants. These industry partners tested the pipeline within a trusted research environment (TRE), using real-world data. All industry partners were able to evaluate skin lesion images in combination with interoperable clinical metadata and run existing algorithms in the TRE. We report progress on this competition and also on the process of creating a national database, to permit competitive testing performance of different algorithms with specific clinical use cases. To assess the cost-effectiveness of introducing AI into the skin cancer pathway, we have also collaborated with the York Health Economics Consortium to produce a model to permit estimation of the impact of an AI triage tool to assist clinician triage. The recent vision statement from the British Association of Dermatologists encourages AI solutions intended to address a clinical unmet need and that integrates into patient pathways to enhance the service provided by healthcare professionals [https://www.bad.org.uk/clinical-services/artificial-intelligence/vision-statement-on-artificial-intelligence-ai-interventions/ (last accessed 19 March 2024)]. If the UK is to be at the forefront of skin AI, then it is imperative that the market is stimulated through use of meticulously standardized interoperable data standards with platforms that allow for transparent testing of multiple algorithms. We have successfully introduced a pipeline for safely generating standardized, high-quality images that are suitable for AI, identifying where we can achieve the greatest potential value for introducing AI into the NHS skin cancer referral pathway

Developing a national artificial intelligence-assisted skin cancer pathway

The AI Skin Cancer Consortium seeks to progress integration of artificial intelligence (AI) into the skin cancer referral pathway to promote rapid diagnosis of skin cancer and reassurance with benign lesions. In contrast to other fields of medicine, such as radiology, dermatology has lacked standardization in data acquisition, which is required for high-quality, reproducible and interoperable AI algorithm performance. Beginning in 2021, the consortium has made substantial progress towards defining a national pathway, technology architecture and data flows. We describe a standardized approach to acquisition of macroscopic and dermoscopic images, and technical and clinical metadata in a DICOM (Digital Imaging and Communications in Medicine) format, currently acquired in outpatient imaging centres and in general practice. In order to develop a transparent competitive environment for testing of this standardized National Health Service (NHS) pipeline, the consortium created a Small Business Research Initiative funded via a government agency. Three companies were awarded funding for this two-phase programme, from 15 applicants. These industry partners tested the pipeline within a trusted research environment (TRE), using real-world data. All industry partners were able to evaluate skin lesion images in combination with interoperable clinical metadata and run existing algorithms in the TRE. We report progress on this competition and also on the process of creating a national database, to permit competitive testing performance of different algorithms with specific clinical use cases. To assess the cost-effectiveness of introducing AI into the skin cancer pathway, we have also collaborated with the York Health Economics Consortium to produce a model to permit estimation of the impact of an AI triage tool to assist clinician triage. The recent vision statement from the British Association of Dermatologists encourages AI solutions intended to address a clinical unmet need and that integrates into patient pathways to enhance the service provided by healthcare professionals [https://www.bad.org.uk/clinical-services/artificial-intelligence/vision-statement-on-artificial-intelligence-ai-interventions/ (last accessed 19 March 2024)]. If the UK is to be at the forefront of skin AI, then it is imperative that the market is stimulated through use of meticulously standardized interoperable data standards with platforms that allow for transparent testing of multiple algorithms. We have successfully introduced a pipeline for safely generating standardized, high-quality images that are suitable for AI, identifying where we can achieve the greatest potential value for introducing AI into the NHS skin cancer referral pathway

Pseudonymization for artificial intelligence skin lesion datasets:a real-world feasibility study

The use of patient data for artificial intelligence (AI) research should be transparent, rigorous and accountable. In the UK, the General Data Protection Regulation, Data Protection Act 2018 and General Medical Council govern data handling and patients’ rights to privacy. We report on our multistep pseudonymization protocol for real-world skin lesion datasets, in preparation for research within a trusted research environment (TRE). Firstly, patients referred from primary care are triaged for community locality and imaging centre (CLIC) suitability. There, trained healthcare professionals capture lesion images (dermoscopic, macroscopic and regional) and patient information using a mobile application on trust-certified devices. Training is standardized across all CLIC sites, with specific anonymization training on removing in-frame clothing and jewellery, device positioning, and magnification to minimize identifiable features like eyes, nose and ears. Lesion datasets (paired images and clinical information) are subsequently transferred to an image management system (IMS) hosted on our trust-secured network. Within the IMS, images are manually inspected, and those with identifiable tattoos and piercings are excluded. All regional images are also excluded from transfer to the TRE. Before transfer to the TRE, images undergo a further round of review. Data fields are manually checked for identifiable patient information, patient names are removed, and dates of birth are rounded to 3-month granularity. The job ID, patient’s hospital number, date of clinical episode and responsible photographer are replaced with randomly generated project-specific identifiers. In an initial study period, 658 of 963 (68%) captured lesion datasets have undergone IMS manual inspection. Of these, 24 lesion datasets were excluded for identifiable features, 10 (41%) for more than one-third of the face being visible, 9 (38%) for full iris visibility, and 5 (21%) for tattoos. On breakdown by anatomical location these images were of the face (19, 80%), torso (2, 8%), limbs (2, 8%) and neck (1, 4%). The remaining 634 datasets (96%) were securely transferred to the TRE, where a further 5% were excluded due to potential identifiability. Although full anonymization is desirable, it is usually achieved by aggregating patient data. Pseudonymization, which allows for future reidentification in a secured fashion, strikes the balance between patient data privacy and clinical governance, while retaining a level of granularity sufficient for meaningful analysis. Currently, this protocol is manually intensive with room to partly automate. Use of common standardized protocols will strengthen the public trust in clinical AI

Photodynamic Therapy in Cutaneous Malignancy

Introduction: The aim of this thesis was to critically evaluate the potential of photodynamic therapy (PDT), using a novel non-laser light source and a topically active photosensitizing agent, 5-aminolaevulinic acid (5-ALA), for use in the management of skin cancer. PDT primarily acts via the activation by visible light, of a photosensitizer located within the neoplastic tissue, to produce activated oxygen species, especially singlet oxygen, in order to promote tumour destruction. Lasers have been the usual source of irradiation in PDT although coherence of light is not required. Recent developments in light technology have enabled the production of a relatively inexpensive non-coherent light source of similar intensity and wavelength to laser. The development of a topically active agent, 5-aminolaevulinic acid (5-ALA), converted within cells to the active photosensitizer, protoporphyrin IX, can now avoid prolonged generalized photosensitivity reactions which limited the development of PDT as a practical clinical therapy. Published clinical data on ALA-PDT available at the commencement of this thesis related predominantly to the use of lasers in open studies, with short term follow-up. The potential of non-laser light had received only limited interest and no randomized comparison trials of PDT had been undertaken. (Abstract shortened by ProQuest.)

Expression of KOC, S100P, mesothelin and MUC1 in pancreatico-biliary adenocarcinomas: development and utility of a potential diagnostic immunohistochemistry panel

<b>Background</b> Pancreatico-biliary adenocarcinomas (PBA) have a poor prognosis. Diagnosis is usually achieved by imaging and/or endoscopy with confirmatory cytology. Cytological interpretation can be difficult especially in the setting of chronic pancreatitis/cholangitis. Immunohistochemistry (IHC) biomarkers could act as an adjunct to cytology to improve the diagnosis. Thus, we performed a meta-analysis and selected KOC, S100P, mesothelin and MUC1 for further validation in PBA resection specimens.<p></p> <b>Methods</b> Tissue microarrays containing tumour and normal cores in a ratio of 3:2, from 99 surgically resected PBA patients, were used for IHC. IHC was performed on an automated platform using antibodies against KOC, S100P, mesothelin and MUC1. Tissue cores were scored for staining intensity and proportion of tissue stained using a Histoscore method (range, 0–300). Sensitivity and specificity for individual biomarkers, as well as biomarker panels, were determined with different cut-offs for positivity and compared by summary receiver operating characteristic (ROC) curve.<p></p> <b>Results</b> The expression of all four biomarkers was high in PBA versus normal ducts, with a mean Histoscore of 150 vs. 0.4 for KOC, 165 vs. 0.3 for S100P, 115 vs. 0.5 for mesothelin and 200 vs. 14 for MUC1 (p < .0001 for all comparisons). Five cut-offs were carefully chosen for sensitivity/specificity analysis. Four of these cut-offs, namely 5%, 10% or 20% positive cells and Histoscore 20 were identified using ROC curve analysis and the fifth cut-off was moderate-strong staining intensity. Using 20% positive cells as a cut-off achieved higher sensitivity/specificity values: KOC 84%/100%; S100P 83%/100%; mesothelin 88%/92%; and MUC1 89%/63%. Analysis of a panel of KOC, S100P and mesothelin achieved 100% sensitivity and 99% specificity if at least 2 biomarkers were positive for 10% cut-off; and 100% sensitivity and specificity for 20% cut-off.<p></p> <b>Conclusion</b> A biomarker panel of KOC, S100P and mesothelin with at least 2 biomarkers positive was found to be an optimum panel with both 10% and 20% cut-offs in resection specimens from patients with PBA.<p></p&gt

Novel derivatives of DPP and related heterocycles

This thesis discusses the synthesis of new organic, heterocyclic materials for potential application as pigments. Chapter 1 comprises an introduction to the field of pigment and dye chemistry, discussing the rudimentary elements of colour theory, before advancing to a review of the pertinent literature regarding high performance organic pigments. In particular, the development of 1,4-diketopyrrolo[3,4-c]pyrrole (DPP) pigments is described and the central objective of synthesising alkenyl-DPPs is outlined. Chapters 2 and 3 describe synthetic efforts towards alkenyl-DPPs, employing retro Diels-Alder methodology. The reactions involving the furan-acrylonitrile adduct as the nitrile component in the standard DPP synthesis led mainly to aromatisation of the bicyclic system and the cyclopentadiene-acrylonitrile adduct failed to react altogether. The explanation for this failure has been investigated. In the course of this these studies, several DPPs incorporating a secondary alkyl substituent were prepared, not least a novel cyclohexenyl-DPP. Chapter 4 describes the use of α β-unsaturated nitriles in the standard DPP synthesis. These behaved as Michael acceptors and in the case of cinnamonitriles led to a new family of coloured materials, namely substituted 4-hydroxy-2/7- cyclopenta[c]pyrrol-1-one-5-carbonitriles. Chapter 5 describes the corresponding reaction of cinnamate esters, but in these cases bicyclic systems were not formed. The reactions are analogous to Claisen acylations and the stereochemistry of the products varied according to the substituents. Chapter 6 contains the detailed experimental work for these investigations and concludes with a portfolio of X-ray structural data

Artificial intelligence-ready skin cancer alchemy:transforming routine teledermatology data into metadata-embedded DICOM files

Most skin artificial intelligence (AI) classifiers are trained only on images with diagnostic labels. However, the addition of clinical information can improve predictive accuracy. Recent interest has been stimulated in incorporating clinical data into image files, using the well-established international Digital Imaging and Communication in Medicine (DICOM) standards (Caffery L, Weber J, Kurtansky N et al. DICOM in dermoscopic research: experience report and a way forward. J Digit Imaging 2021; 34: 967–73). We have developed an automated process of creating metadata-embedded DICOM files, directly from a live teledermatology system, described below. Through our Community and Locality Imaging Centre (CLIC) model, patients referred from primary care are triaged to CLIC for high-quality image capture. There, trained health professionals use a mobile application to capture standardized DICOM information for each lesion. Each lesion dataset contains images (macroscopic, dermoscopic) and clinical metadata (patient and lesion information). Datasets are transferred to an image management system, for teledermatology and verification of ground-truth diagnoses by a consultant dermatologist. On completion of diagnoses, datasets are flagged for conversion into DICOM format, where metadata are embedded in the image files. Flagged datasets are cleaned and clinical metadata are mapped to DICOM attributes. Datasets are converted into metadata-embedded DICOM files, and reviewed for conformance to the DICOM standard using the open-source fo-dicom library (v5). These files are further tested for conformance to DICOM standard using the dciodvfy validator tool. Compliant DICOM files are then transferred to a trusted research environment for research. To test whether these DICOM files are usable for AI research, they are examined using the DICOM viewing software 3D Slicer (https://www.slicer.org/), ensuring images are usable and metadata are correctly translated. Image pixel data and clinical metadata are extracted using pydicom, into a format suitable for AI algorithm development. In our pilot work, 658 lesion datasets have been converted into metadata-embedded DICOM files. Conversion on existing hardware [virtual Intel central processing units with 2.60 GHz (two processors) and 8 GB of memory] took < 1 s per image. Metadata-embedded DICOM files were approximately 0.2 kB bigger than the original JPEG files. For 3-MB images, this represented a negligible 0.003% increase in storage requirement. Testing has shown that these files can be successfully handled by algorithms within an AI research environment. In summary, we have demonstrated the feasibility of automating the conversion of routine teledermatology data into AI-ready image files encoded with clinical metadata. Future work is planned to evaluate the utility of this output on the performance of AI classifiers

2-Vector Spaces and Groupoids

This paper describes a relationship between essentially finite groupoids and 2-vector spaces. In particular, we show to construct 2-vector spaces of Vect-valued presheaves on such groupoids. We define 2-linear maps corresponding to functors between groupoids in both a covariant and contravariant way, which are ambidextrous adjoints. This is used to construct a representation--a weak functor--from Span(Gpd) (the bicategory of groupoids and spans of groupoids) into 2Vect. In this paper we prove this and give the construction in detail.Comment: 44 pages, 5 figures - v2 adds new theorem, significant changes to proofs, new sectio