Development of a Non-Melanoma Skin Cancer Detection Model

Geer, van der S; Kleingeld, Pam Ad; Snijders, Chris; Rinkens, Fjch Frank; Jansen, Gae Geert; Neumann, H. A. Martino; Krekels, Gertruud A. M.

doi:10.1159/000369790

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…Our current approach is distinguished from a previous study that utilized logistic regression for NMSC diagnosis by analyzing survey data from a much smaller sample size, and that also contained radiation exposure (e.g. sun exposure, sun burn history, tanning salon usage,) and family history 25 . This survey-based analysis was limited to ~200 adults who were referred by their primary physicians for suspicious skin lesions, e.g.…”

Section: Discussionmentioning

confidence: 99%

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network

Roffman

Hart

Girardi

et al. 2018

Sci Rep

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Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The objective of this study was to develop and validate a multi-parameterized artificial neural network based on available personal health information for early detection of NMSC with high sensitivity and specificity, even in the absence of known UVR exposure and family history. The 1997–2015 NHIS adult survey data used to train and validate our neural network (NN) comprised of 2,056 NMSC and 460,574 non-cancer cases. We extracted 13 parameters for our NN: gender, age, BMI, diabetic status, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. This study yielded an area under the ROC curve of 0.81 and 0.81 for training and validation, respectively. Our results (training sensitivity 88.5% and specificity 62.2%, validation sensitivity 86.2% and specificity 62.7%) were comparable to a previous study of basal and squamous cell carcinoma prediction that also included UVR exposure and family history information. These results indicate that our NN is robust enough to make predictions, suggesting that we have identified novel associations and potential predictive parameters of NMSC.

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Section: Discussionmentioning

confidence: 99%

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network

Roffman

Hart

Girardi

et al. 2018

Sci Rep

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“…We included four phenotypic factors, namely age at AK diagnosis in the RS (years), [12][13][14]17,22,23 sex, [12][13][14]17,18,22 tendency to develop sunburn 8,[12][13][14]17,20,24,25 and pigment status. 8,18,24,26 The latter constituted a combination of hair and eye colour when young, as reported previously.…”

Section: Candidate Predictor Variablesmentioning

confidence: 99%

Predicting keratinocyte carcinoma in patients with actinic keratosis: development and internal validation of a multivariable risk‐prediction model

et al. 2020

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Summary Background Patients with actinic keratosis (AK) are at increased risk for developing keratinocyte carcinoma (KC) but predictive factors and their risk rates are unknown. Objectives To develop and internally validate a prediction model to calculate the absolute risk of a first KC in patients with AK. Methods The risk‐prediction model was based on the prospective population‐based Rotterdam Study cohort. We hereto analysed the data of participants with at least one AK lesion at cohort baseline using a multivariable Cox proportional hazards model and included 13 a priori defined candidate predictor variables considering phenotypic, genetic and lifestyle risk factors. KCs were identified by linkage of the data with the Dutch Pathology Registry. Results Of the 1169 AK participants at baseline, 176 (15·1%) developed a KC after a median follow‐up of 1·8 years. The final model with significant predictors was obtained after backward stepwise selection and comprised the presence of four to nine AKs [hazard ratio (HR) 1·68, 95% confidence interval (CI) 1·17–2·42], 10 or more AKs (HR 2·44, 95% CI 1·65–3·61), AK localization on the upper extremities (HR 0·75, 95% CI 0·52–1·08) or elsewhere except the head (HR 1·40, 95% CI 0·98–2·01) and coffee consumption (HR 0·92, 95% CI 0·84–1·01). Evaluation of the discriminative ability of the model showed a bootstrap validated concordance index (c‐index) of 0·60. Conclusions We showed that the risk of KC in patients with AK can be calculated with the use of four easily assessable predictor variables. Given the c‐index, extension of the model with additional, currently unknown predictor variables is desirable. Linked Comment: Kim et al. Br J Dermatol 2020; 183:415–416.

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“…In order to broaden the knowledge about this disease, a wide range of machine learning (ML) and computer science approaches have been proposed: neural networks [ 11 ], image preprocessing and classification [ 12 – 14 ], prediction models [ 15 , 16 ], pattern recognition [ 17 ], optical techniques [ 18 , 19 ], etc. Although each approach focuses on improving the skin cancer diagnosis by using different techniques, a comprehensive analysis of the gene expression can extract revealing genes which could be responsible for a number of manifestations of this genetic disease [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

et al. 2018

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Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.

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Development of a Non-Melanoma Skin Cancer Detection Model

Cited by 5 publications

References 17 publications

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network

Predicting keratinocyte carcinoma in patients with actinic keratosis: development and internal validation of a multivariable risk‐prediction model

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

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