Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation
Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatica...
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| Vydáno v: | Cancers Ročník 15; číslo 18; s. 4457 |
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07.09.2023
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| Abstract | Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts’ visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients. |
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| AbstractList | Simple SummaryIn this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical coherence tomography (LC-OCT) to diagnose actinic keratosis (AK). The AI system accurately graded AK lesions in a large patient cohort and showed high agreement with visual assessments by experts. This non-invasive and fast AI-based approach has the potential to improve the efficiency and accuracy of AK diagnosis, leading to better clinical outcomes for patients.AbstractActinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts’ visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients. Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction's (DEJ's) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts' visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients.Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction's (DEJ's) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts' visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients. In this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical coherence tomography (LC-OCT) to diagnose actinic keratosis (AK). The AI system accurately graded AK lesions in a large patient cohort and showed high agreement with visual assessments by experts. This non-invasive and fast AI-based approach has the potential to improve the efficiency and accuracy of AK diagnosis, leading to better clinical outcomes for patients. Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts’ visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients. In this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical coherence tomography (LC-OCT) to diagnose actinic keratosis (AK). The AI system accurately graded AK lesions in a large patient cohort and showed high agreement with visual assessments by experts. This non-invasive and fast AI-based approach has the potential to improve the efficiency and accuracy of AK diagnosis, leading to better clinical outcomes for patients. Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts’ visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients. |
| Audience | Academic |
| Author | Maximilian Deußing Daniela Hartmann Janis Thamm Elke Sattler Sandra Schuh Julia Welzel Quirine Eijkenboom Fabia Daxenberger Charlotte Gust Lars French |
| AuthorAffiliation | 3 Department of Dermatology & Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA 1 Department of Dermatology and Allergy, University Hospital, Ludwig Maximilian University of Munich, 80337 Munich, Germany elke.sattler@med.uni-muenchen.de (E.C.S.) 2 Department of Dermatology and Allergology, University Hospital, University of Augsburg, 86179 Augsburg, Germany |
| AuthorAffiliation_xml | – name: 1 Department of Dermatology and Allergy, University Hospital, Ludwig Maximilian University of Munich, 80337 Munich, Germany elke.sattler@med.uni-muenchen.de (E.C.S.) – name: 3 Department of Dermatology & Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA – name: 2 Department of Dermatology and Allergology, University Hospital, University of Augsburg, 86179 Augsburg, Germany |
| Author_xml | – sequence: 1 givenname: Fabia surname: Daxenberger fullname: Daxenberger, Fabia – sequence: 2 givenname: Maximilian orcidid: 0000-0003-4935-9877 surname: Deußing fullname: Deußing, Maximilian – sequence: 3 givenname: Quirine orcidid: 0009-0006-8014-4152 surname: Eijkenboom fullname: Eijkenboom, Quirine – sequence: 4 givenname: Charlotte surname: Gust fullname: Gust, Charlotte – sequence: 5 givenname: Janis surname: Thamm fullname: Thamm, Janis – sequence: 6 givenname: Daniela orcidid: 0000-0002-1002-8133 surname: Hartmann fullname: Hartmann, Daniela – sequence: 7 givenname: Lars surname: French fullname: French, Lars – sequence: 8 givenname: Julia orcidid: 0000-0002-6099-7418 surname: Welzel fullname: Welzel, Julia – sequence: 9 givenname: Sandra orcidid: 0000-0002-1470-7619 surname: Schuh fullname: Schuh, Sandra – sequence: 10 givenname: Elke surname: Sattler fullname: Sattler, Elke |
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| Cites_doi | 10.1111/ced.14762 10.1111/bjd.17632 10.1038/nature21056 10.1111/jdv.13626 10.1016/j.jmir.2019.09.005 10.1002/jbio.201800391 10.1002/cac2.12012 10.1136/ijgc-2022-004213 10.1096/fj.202001412R 10.1111/j.1365-2133.2007.07860.x 10.1111/jdv.14512 10.1016/S0140-6736(88)91658-3 10.1038/s43018-022-00436-4 10.1007/s13244-018-0639-9 10.1001/jamadermatol.2022.1034 10.1111/jdv.12848 10.3390/cancers13122856 10.3390/ijerph182413409 10.1016/j.jaad.2022.12.057 10.1016/j.clindermatol.2021.03.011 10.1111/bjd.16536 10.1111/ajd.13965 10.5826/dpc.1004a121 10.1111/ddg.15194 10.1111/jdv.15580 10.1067/mjd.2000.103344 10.1111/j.1365-2133.2006.07426.x 10.1007/s40257-019-00462-6 |
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| References | Noels (ref_30) 2019; 181 Cockerell (ref_2) 2000; 42 Christensen (ref_4) 2023; 88 Schmitz (ref_9) 2018; 32 ref_13 Schmitz (ref_23) 2019; 33 Queen (ref_32) 2020; 34 Carrato (ref_8) 2015; 29 Ruini (ref_14) 2021; 46 ref_19 Currie (ref_21) 2019; 50 Esteva (ref_18) 2017; 542 ref_17 ref_16 Yamashita (ref_20) 2018; 9 Zalaudek (ref_11) 2006; 155 Siddharthan (ref_6) 2019; 32 Hogarty (ref_27) 2020; 21 Kesic (ref_5) 2023; 33 Schmitz (ref_12) 2019; 180 ref_22 Schmitz (ref_1) 2018; 16 Shmatko (ref_24) 2022; 3 Marks (ref_28) 1988; 1 Rundle (ref_26) 2021; 39 Heppt (ref_3) 2020; 18 Schmitz (ref_10) 2016; 30 Patel (ref_7) 2007; 156 Jiang (ref_25) 2020; 40 Ahmady (ref_29) 2022; 158 Suppa (ref_15) 2023; 158 Kim (ref_31) 2023; 64 |
| References_xml | – volume: 46 start-page: 1471 year: 2021 ident: ref_14 article-title: Line-field optical coherence tomography: In vivo diagnosis of basal cell carcinoma subtypes compared with histopathology publication-title: Clin. Exp. Dermatol. doi: 10.1111/ced.14762 – volume: 181 start-page: 544 year: 2019 ident: ref_30 article-title: Healthcare utilization and management of actinic keratosis in primary and secondary care: A complementary database analysis publication-title: Br. J. Dermatol. doi: 10.1111/bjd.17632 – volume: 158 start-page: 180 year: 2023 ident: ref_15 article-title: Line-field confocal optical coherence tomography in melanocytic and non-melanocytic skin tumors publication-title: Ital. J. Dermatol. Venerol. – volume: 542 start-page: 115 year: 2017 ident: ref_18 article-title: Dermatologist-level classification of skin cancer with deep neural networks publication-title: Nature doi: 10.1038/nature21056 – volume: 30 start-page: 1303 year: 2016 ident: ref_10 article-title: Actinic keratosis: Correlation between clinical and histological classification systems publication-title: J. Eur. Acad. Dermatol. Venereol. doi: 10.1111/jdv.13626 – volume: 50 start-page: 477 year: 2019 ident: ref_21 article-title: Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging publication-title: J. Med. Imaging Radiat. Sci. doi: 10.1016/j.jmir.2019.09.005 – ident: ref_16 doi: 10.1002/jbio.201800391 – volume: 40 start-page: 154 year: 2020 ident: ref_25 article-title: Emerging role of deep learning-based artificial intelligence in tumor pathology publication-title: Cancer Commun. doi: 10.1002/cac2.12012 – volume: 33 start-page: 446 year: 2023 ident: ref_5 article-title: The European Society of Gynaecological Oncology (ESGO), the International Society for the Study of Vulvovaginal Disease (ISSVD), the European College for the Study of Vulval Disease (ECSVD), and the European Federation for Colposcopy (EFC) consensus statement on the management of vaginal intraepithelial neoplasia publication-title: Int. J. Gynecol. Cancer doi: 10.1136/ijgc-2022-004213 – volume: 32 start-page: 257 year: 2019 ident: ref_6 article-title: Anal intraepithelial neoplasia: Diagnosis, screening, and treatment publication-title: Ann. Gastroenterol. – volume: 34 start-page: 13022 year: 2020 ident: ref_32 article-title: UV biomarker genes for classification and risk stratification of cutaneous actinic keratoses and squamous cell carcinoma subtypes publication-title: FASEB J. doi: 10.1096/fj.202001412R – volume: 156 start-page: 8 year: 2007 ident: ref_7 article-title: Actinic keratosis is an early in situ squamous cell carcinoma: A proposal for reclassification publication-title: Br. J. Dermatol. doi: 10.1111/j.1365-2133.2007.07860.x – volume: 32 start-page: 745 year: 2018 ident: ref_9 article-title: Actinic keratoses show variable histological basal growth patterns—A proposed classification adjustment publication-title: J. Eur. Acad. Dermatol. Venereol. doi: 10.1111/jdv.14512 – volume: 1 start-page: 795 year: 1988 ident: ref_28 article-title: Malignant transformation of solar keratoses to squamous cell carcinoma publication-title: Lancet doi: 10.1016/S0140-6736(88)91658-3 – volume: 3 start-page: 1026 year: 2022 ident: ref_24 article-title: Artificial intelligence in histopathology: Enhancing cancer research and clinical oncology publication-title: Nat. Cancer doi: 10.1038/s43018-022-00436-4 – volume: 9 start-page: 611 year: 2018 ident: ref_20 article-title: Convolutional neural networks: An overview and application in radiology publication-title: Insights Imaging doi: 10.1007/s13244-018-0639-9 – volume: 158 start-page: 634 year: 2022 ident: ref_29 article-title: Risk of Invasive Cutaneous Squamous Cell Carcinoma after Different Treatments for Actinic Keratosis: A Secondary Analysis of a Randomized Clinical Trial publication-title: JAMA Dermatol. doi: 10.1001/jamadermatol.2022.1034 – volume: 29 start-page: 991 year: 2015 ident: ref_8 article-title: Actinic keratosis with atypical basal cells (AK I) is the most common lesion associated with invasive squamous cell carcinoma of the skin publication-title: J. Eur. Acad. Dermatol. Venereol. doi: 10.1111/jdv.12848 – ident: ref_17 doi: 10.3390/cancers13122856 – ident: ref_19 doi: 10.3390/ijerph182413409 – volume: 18 start-page: 275 year: 2020 ident: ref_3 article-title: S3 guideline for actinic keratosis and cutaneous squamous cell carcinoma—Short version, part 1: Diagnosis, interventions for actinic keratoses, care structures and quality-of-care indicators publication-title: J. Dtsch. Dermatol. Ges. – volume: 88 start-page: 1317 year: 2023 ident: ref_4 article-title: Dermatopathologic features of cutaneous squamous cell carcinoma and actinic keratosis: Consensus criteria and proposed reporting guidelines publication-title: J. Am. Acad. Dermatol. doi: 10.1016/j.jaad.2022.12.057 – volume: 39 start-page: 657 year: 2021 ident: ref_26 article-title: Artificial intelligence in dermatology publication-title: Clin. Dermatol. doi: 10.1016/j.clindermatol.2021.03.011 – volume: 180 start-page: 916 year: 2019 ident: ref_12 article-title: Cutaneous squamous cell carcinomas are associated with basal proliferating actinic keratoses publication-title: Br. J. Dermatol. doi: 10.1111/bjd.16536 – volume: 64 start-page: 80 year: 2023 ident: ref_31 article-title: Factors for risk stratification of patients with actinic keratosis using integrated analysis of clinicopathological features and gene expression patterns publication-title: Australas. J. Dermatol. doi: 10.1111/ajd.13965 – ident: ref_13 doi: 10.5826/dpc.1004a121 – ident: ref_22 doi: 10.1111/ddg.15194 – volume: 33 start-page: 1092 year: 2019 ident: ref_23 article-title: Evaluation of two histological classifications for actinic keratoses—PRO classification scored highest inter-rater reliability publication-title: J. Eur. Acad. Dermatol. Venereol. doi: 10.1111/jdv.15580 – volume: 42 start-page: 11 year: 2000 ident: ref_2 article-title: Histopathology of incipient intraepidermal squamous cell carcinoma (“actinic keratosis”) publication-title: J. Am. Acad. Dermatol. doi: 10.1067/mjd.2000.103344 – volume: 155 start-page: 951 year: 2006 ident: ref_11 article-title: Dermoscopy of facial nonpigmented actinic keratosis publication-title: Br. J. Dermatol. doi: 10.1111/j.1365-2133.2006.07426.x – volume: 16 start-page: 1002 year: 2018 ident: ref_1 article-title: Nonmelanoma skin cancer—From actinic keratosis to cutaneous squamous cell carcinoma publication-title: JDDG J. Dtsch. Dermatol. Ges. – volume: 21 start-page: 41 year: 2020 ident: ref_27 article-title: Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review publication-title: Am. J. Clin. Dermatol. doi: 10.1007/s40257-019-00462-6 |
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| Snippet | Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged... In this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal optical... Simple SummaryIn this study, we evaluated the performance of a previous validated artificial intelligence-based assessment algorithm using line-field confocal... |
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| SubjectTerms | Actinic keratosis Algorithms Artificial intelligence Classification Data mining ddc:610 Dermatology Diagnosis Histology Invasiveness Keratosis Lesions Machine learning Neural networks Pilot projects Skin Skin cancer Tomography |
| Title | Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation |
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