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Guryleva A, Machikhin A, Orlova E, Kulikova E, Volkov M, Gabrielian G, Smirnova L, Sekacheva M, Olisova O, Rudenko E, Lobanova O, Smolyannikova V, Demura T. Photoplethysmography-Based Angiography of Skin Tumors in Arbitrary Areas of Human Body. JOURNAL OF BIOPHOTONICS 2024:e202400242. [PMID: 39327652 DOI: 10.1002/jbio.202400242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 09/28/2024]
Abstract
Noninvasive, rapid, and robust diagnostic techniques for clinical screening of tumors located in arbitrary areas of the human body are in demand. To address this challenge, we analyzed the feasibility of photoplethysmography-based angiography for assessing vascular structures within malignant and benign tumors. The proposed hardware and software were approved in a clinical study involving 30 patients with tumors located in the legs, torso, arms, and head. High-contrast and detailed vessel maps within both benign and malignant tumors were obtained. We demonstrated that capillary maps are consistent and can be interpreted using well-established dermoscopic criteria for vascular morphology. Vessel mapping provides valuable details, which may not be available in dermoscopic images and can aid in determining whether a tumor is benign or malignant. We believe that the proposed approach may become a valuable tool in the preliminary cancer diagnosis and is suitable for large-scale screening.
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Affiliation(s)
- Anastasia Guryleva
- Scientific and Technological Centre of Unique Instrumentation of Russian Academy of Sciences, Moscow, Russia
| | - Alexander Machikhin
- Scientific and Technological Centre of Unique Instrumentation of Russian Academy of Sciences, Moscow, Russia
| | - Ekaterina Orlova
- V.A. Rakhmanov Department of Dermatology and Venereology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Evgeniya Kulikova
- Scientific and Technological Centre of Unique Instrumentation of Russian Academy of Sciences, Moscow, Russia
| | - Michail Volkov
- Scientific and Technological Centre of Unique Instrumentation of Russian Academy of Sciences, Moscow, Russia
| | - Gaiane Gabrielian
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Ludmila Smirnova
- V.A. Rakhmanov Department of Dermatology and Venereology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Marina Sekacheva
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Olga Olisova
- V.A. Rakhmanov Department of Dermatology and Venereology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Ekaterina Rudenko
- Institute of Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Olga Lobanova
- Institute of Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Vera Smolyannikova
- Institute of Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Tatiana Demura
- Institute of Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
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Gouveia BM, Carlos G, Wadell A, Sinz C, Ahmed T, Lo SN, Rawson RV, Ferguson PM, Scolyer RA, Guitera P. In vivo reflectance confocal microscopy can detect the invasive component of lentigo maligna melanoma: Prospective analysis and case-control study. J Eur Acad Dermatol Venereol 2023; 37:1293-1301. [PMID: 36855833 PMCID: PMC10946995 DOI: 10.1111/jdv.18998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/08/2023] [Indexed: 03/02/2023]
Abstract
BACKGROUND Lentigo maligna (LM), a form of melanoma in situ, has no risk of causing metastasis unless dermal invasive melanoma (LMM) supervenes. Furthermore, the detection of invasion impacts prognosis and management. OBJECTIVE To assess the accuracy of RCM for the detection of invasion component on LM/LMM lesions. METHODS In the initial case-control study, the performance of one expert in detecting LMM at the time of initial RCM assessment of LM/LMM lesions was recorded prospectively (n = 229). The cases were assessed on RCM-histopathology correlation sessions and a panel with nine RCM features was proposed to identify LMM, which was subsequently tested in a subset of initial cohort (n = 93) in the matched case-control study by two blinded observers. Univariable and multivariable logistic regression models were performed to evaluate RCM features predictive of LMM. Reproducibility of assessment of the nine RCM features was also evaluated. RESULTS A total of 229 LM/LMM cases evaluated by histopathology were assessed blindly and prospectively by an expert confocalist. On histopathology, 210 were LM and 19 were LMM cases. Correct identification of an invasive component was achieved for 17 of 19 LMM cases (89%) and the absence of a dermal component was correctly diagnosed in 190 of 210 LM cases (90%). In the matched case-control (LMM n = 35, LM n = 58), epidermal and junctional disarray, large size of melanocytes and nests of melanocytes were independent predictors of LMM on multivariate analysis. The interobserver analysis demonstrated that these three features had a fair reproducibility between the two investigators (K = 0.4). The multivariable model including those three features showed a high predictive performance AUC = 74% (CI 95% 64-85%), with sensitivity of 63% (95% CI 52-78%) and specificity of 79% (CI 95% 74-88%), and likelihood ratio of 18 (p-value 0.0026). CONCLUSION Three RCM features were predictive for identifying invasive melanoma in the background of LM.
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Grants
- Melanoma Institute Australia
- N/A MetaOptima Technology Incl., Hoffmann-La Roche Ltd, Evaxion, Provectus Biopharmaceuticals Australia, Qbiotics, Novartis, Merck Sharp & Dohme, NeraCare, AMGEN Inc., Bristol-Myers Squibb, Myriad Genetics, GlaxoSmithKline.
- APP1141295 NHMRC Practitioner Fellowship
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Affiliation(s)
- Bruna Melhoranse Gouveia
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Giuliana Carlos
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Andreanne Wadell
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- CHUS - Hôtel-Dieu, Sherbrooke, Québec, Canada
| | - Christoph Sinz
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Tasnia Ahmed
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Serigne N Lo
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Robert V Rawson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | - Peter M Ferguson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Pascale Guitera
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
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Mandal A, Priyam S, Chan HH, Gouveia BM, Guitera P, Song Y, Baker MAB, Vafaee F. Computer-Aided Diagnosis of Melanoma Subtypes Using Reflectance Confocal Images. Cancers (Basel) 2023; 15:1428. [PMID: 36900219 PMCID: PMC10000703 DOI: 10.3390/cancers15051428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
Lentigo maligna (LM) is an early form of pre-invasive melanoma that predominantly affects sun-exposed areas such as the face. LM is highly treatable when identified early but has an ill-defined clinical border and a high rate of recurrence. Atypical intraepidermal melanocytic proliferation (AIMP), also known as atypical melanocytic hyperplasia (AMH), is a histological description that indicates melanocytic proliferation with uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may, in some cases, progress to LM. The early diagnosis and distinction of LM from AIMP are important since LM requires a definitive treatment. Reflectance confocal microscopy (RCM) is an imaging technique often used to investigate these lesions non-invasively, without biopsy. However, RCM equipment is often not readily available, nor is the associated expertise for RCM image interpretation easy to find. Here, we implemented a machine learning classifier using popular convolutional neural network (CNN) architectures and demonstrated that it could correctly classify lesions between LM and AIMP on biopsy-confirmed RCM image stacks. We identified local z-projection (LZP) as a recent fast approach for projecting a 3D image into 2D while preserving information and achieved high-accuracy machine classification with minimal computational requirements.
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Affiliation(s)
- Ankita Mandal
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
- Department of Mechanical Engineering, Indian Institute of Technology (IIT Delhi), Delhi 110016, India
| | - Siddhaant Priyam
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
- Department of Electrical Engineering, Indian Institute of Technology (IIT Delhi), Delhi 110016, India
| | - Hsien Herbert Chan
- Department of Dermatology, Princess Alexandra Hospital, Brisbane 4102, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney 2006, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney 2006, Australia
| | - Bruna Melhoranse Gouveia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney 2006, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney 2006, Australia
| | - Pascale Guitera
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney 2006, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney 2006, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
| | | | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
- UNSW Data Science Hub, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
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Von Knorring T, Israelsen NM, Ung V, Formann JL, Jensen M, Hædersdal M, Bang O, Fredman G, Mogensen M. Bedside Differentiation Between Benign and Malignant Pigmented Skin Tumours by Four Diagnostic Imaging Technologies - A Pilot Study. Acta Derm Venereol 2021; 102:adv00634. [PMID: 34806755 DOI: 10.2340/actadv.v101.571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Fast diagnosis of suspicious pigmented skin lesions is imperative, but current bedside skin imaging technologies are either limited in penetration depth or resolution. Combining imaging methods is therefore highly relevant for skin cancer diagnostics. This pilot study evaluates the ability of optical coherence tomography, reflectance confocal microscopy, photoacoustic imaging and high-frequency ultrasound to differentiate malignant from benign pigmented skin lesions. A total of 41 pigmented skin tumours were scanned prior to excision. Morphologic features and blood vessel characteristics were analysed in reflectance confocal microscopy, optical coherence tomography, high-frequency ultrasound and photoacoustic imaging images and diagnostic accuracy assessed. Three novel photoacoustic imaging features, 7 reflectance confocal microscopy features and two optical coherence tomography features were detected with a high correlation to malignancy, diagnostic accuracy > 71%. No significant features were found in high-frequency ultrasound. Conclusively, optical coherence tomography, reflectance confocal microscopy and photoacoustic imaging in combination enables image-guided evaluation of suspicious pigmented skin tumours at the bedside. Combining these advanced techniques may help to diagnose skin cancer more efficiently.
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Affiliation(s)
- Terese Von Knorring
- Department of Dermatology, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Nielsine Nielsens Vej 17, DK-2400 Copenhagen, Denmark.
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