A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi
•The diagnosis of Atypical melanocytic skin lesions (aMSL) is worldwide challenging.•Deep convolutional neural network (DCNN) provide high accuracy in image recognition.•We developed the first hybrid DCNN model trained with aMSL images and clinical data (maximum diameter, site, age, sex).•Dermoscopi...
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| Vydané v: | Journal of dermatological science Ročník 101; číslo 2; s. 115 - 122 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
01.02.2021
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| ISSN: | 0923-1811, 1873-569X, 1873-569X |
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| Abstract | •The diagnosis of Atypical melanocytic skin lesions (aMSL) is worldwide challenging.•Deep convolutional neural network (DCNN) provide high accuracy in image recognition.•We developed the first hybrid DCNN model trained with aMSL images and clinical data (maximum diameter, site, age, sex).•Dermoscopists would increase the diagnostic accuracy by consulting the iDCNN_aMSL.•The iDCNN_aMSL would reduce the number of inappropriate excision of benign aMSL.
Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists’ experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM).
We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL).
A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated “iDCNN_aMSL” model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models.
In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %).
The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions. |
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| AbstractList | Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM).BACKGROUNDTimely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM).We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL).OBJECTIVEWe aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL).A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated "iDCNN_aMSL" model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models.METHODSA training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated "iDCNN_aMSL" model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models.In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %).RESULTSIn the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %).The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions.CONCLUSIONSThe iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions. •The diagnosis of Atypical melanocytic skin lesions (aMSL) is worldwide challenging.•Deep convolutional neural network (DCNN) provide high accuracy in image recognition.•We developed the first hybrid DCNN model trained with aMSL images and clinical data (maximum diameter, site, age, sex).•Dermoscopists would increase the diagnostic accuracy by consulting the iDCNN_aMSL.•The iDCNN_aMSL would reduce the number of inappropriate excision of benign aMSL. Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists’ experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM). We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL). A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated “iDCNN_aMSL” model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models. In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %). The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions. Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM). We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL). A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated "iDCNN_aMSL" model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models. In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %). The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions. |
| Author | Tiodorovic, Danica Balistreri, Alberto Cinotti, Elisa Gori, Marco Cevenini, Gabriele Carrera, Cristina Argenziano, Giuseppe Rubegni, Pietro Bonechi, Simone Longo, Caterina Scarselli, Franco Puig, Susana Farnetani, Francesca Cataldo, Gennaro Moscarella, Elvira Tognetti, Linda Lallas, Aimilios Perrot, Jean Luc Bianchini, Monica Cartocci, Alessandra Mecocci, Alessandro Andreini, Paolo Pellacani, Giovanni |
| Author_xml | – sequence: 1 givenname: Linda surname: Tognetti fullname: Tognetti, Linda email: linda.tognetti@dbm.unisi.it organization: Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy – sequence: 2 givenname: Simone surname: Bonechi fullname: Bonechi, Simone organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 3 givenname: Paolo surname: Andreini fullname: Andreini, Paolo organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 4 givenname: Monica surname: Bianchini fullname: Bianchini, Monica organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 5 givenname: Franco surname: Scarselli fullname: Scarselli, Franco organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 6 givenname: Gabriele surname: Cevenini fullname: Cevenini, Gabriele organization: Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy – sequence: 7 givenname: Elvira surname: Moscarella fullname: Moscarella, Elvira organization: Dermatology Unit, University of Campania Luigi Vanvitelli, Naples, Italy – sequence: 8 givenname: Francesca surname: Farnetani fullname: Farnetani, Francesca organization: Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy – sequence: 9 givenname: Caterina surname: Longo fullname: Longo, Caterina organization: Centro Oncologico ad Alta Tecnologia Diagnostica, Azienda Unità Sanitaria Locale, IRCCS di Reggio Emilia, Reggio Emilia, Italy – sequence: 10 givenname: Aimilios surname: Lallas fullname: Lallas, Aimilios organization: First Department of Dermatology, Aristotle University, Thessaloniki, Greece – sequence: 11 givenname: Cristina surname: Carrera fullname: Carrera, Cristina organization: Melanoma Unit, Department of Dermatology, University of Barcelona, Barcelona, Spain – sequence: 12 givenname: Susana surname: Puig fullname: Puig, Susana organization: Melanoma Unit, Department of Dermatology, University of Barcelona, Barcelona, Spain – sequence: 13 givenname: Danica surname: Tiodorovic fullname: Tiodorovic, Danica organization: Dermatology Clinic, Medical Faculty, Nis University, Nis, Serbia – sequence: 14 givenname: Jean Luc surname: Perrot fullname: Perrot, Jean Luc organization: Dermatology Unit, University Hospital of St-Etienne, Saint Etienne, France – sequence: 15 givenname: Giovanni surname: Pellacani fullname: Pellacani, Giovanni organization: Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy – sequence: 16 givenname: Giuseppe surname: Argenziano fullname: Argenziano, Giuseppe organization: Dermatology Unit, University of Campania Luigi Vanvitelli, Naples, Italy – sequence: 17 givenname: Elisa surname: Cinotti fullname: Cinotti, Elisa organization: Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy – sequence: 18 givenname: Gennaro surname: Cataldo fullname: Cataldo, Gennaro organization: Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy – sequence: 19 givenname: Alberto surname: Balistreri fullname: Balistreri, Alberto organization: Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy – sequence: 20 givenname: Alessandro surname: Mecocci fullname: Mecocci, Alessandro organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 21 givenname: Marco surname: Gori fullname: Gori, Marco organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 22 givenname: Pietro surname: Rubegni fullname: Rubegni, Pietro organization: Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy – sequence: 23 givenname: Alessandra surname: Cartocci fullname: Cartocci, Alessandra organization: Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33358096$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 Japanese Society for Investigative Dermatology Copyright © 2020 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved. |
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| Keywords | DCNN_aMSL ST Cutaneous melanoma ROC aMSL EM DCNNs Dermoscopy Non-invasive imaging AN AUC CNNs Deep learning SE Deep convolutional neural network SP iDCNN_aMSL Integrated diagnosis |
| Language | English |
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| SubjectTerms | Cutaneous melanoma Deep convolutional neural network Deep learning Dermoscopy Integrated diagnosis Non-invasive imaging |
| Title | A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi |
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