Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
Journal of Medical Internet Research, vol. 23, pp. e20708
Abstract
BACKGROUND: Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers. OBJECTIVE: This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance. METHODS: Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined. RESULTS: A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier. CONCLUSIONS: This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients' benefits.
Authors 24
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases · Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, RWTH University Hospital Aachen, Aachen, Germany
National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · University Medical Centre Mannheim
Affiliation as printed
Department of Dermatology, University Hospital of Mannheim, Mannheim, Germany
Skin Cancer Unit, German Cancer Research Center, Heidelberg, Germany
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University Hospital Carl Gustav Carus · Nationales Centrum für Tumorerkrankungen Dresden · Technische Universität Dresden
Affiliation as printed
Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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University Hospital Carl Gustav Carus · Nationales Centrum für Tumorerkrankungen Dresden · Technische Universität Dresden
Affiliation as printed
Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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University Hospital Schleswig-Holstein
Affiliation as printed
Department of Dermatology, University Hospital of Kiel, Kiel, Germany
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Ludwig-Maximilians-Universität München
Affiliation as printed
Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
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Ludwig-Maximilians-Universität München
Affiliation as printed
Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
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Charité - Universitätsmedizin Berlin
Affiliation as printed
Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Berlin, Germany
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Charité - Universitätsmedizin Berlin
Affiliation as printed
Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Berlin, Germany
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Dermatopathologie Friedrichshafen
Affiliation as printed
Dermatopathology Laboratory, Friedrichshafen, Germany
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Affiliation as printed
Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
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University Hospital Regensburg
Affiliation as printed
Department of Dermatology, University Hospital of Regensburg, Regensburg, Germany
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Affiliation as printed
Department of Dermatology, University Hospital Essen, Essen, Germany
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Affiliation as printed
Department of Dermatology, University Hospital Essen, Essen, Germany
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Affiliation as printed
Department of Dermatology, University Hospital Würzburg, Würzburg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center, Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases · Otto-von-Guericke-Universität Magdeburg
Affiliation as printed
Faculty of Medicine, Medical Center, Otto-von-Guericke-University, Magdeburg, Germany
National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center, Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases
Affiliation as printed
Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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