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. 2018 Jan 26;8(1):1701.
doi: 10.1038/s41598-018-19907-9.

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

David Roffman  1 , Gregory Hart  1 , Michael Girardi  2 , Christine J Ko  2 , Jun Deng  3
Affiliations

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

David Roffman et al. Sci Rep. .

Abstract

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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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
A Schematic of the ANN. Each line is weight connecting one layer to next, with each circle representing an input, neuron, or output. The bias terms are analogous to intercepts and improve the model’s performance.
Figure 2
Figure 2
The sensitivity and specificity for the training and validation datasets as functions of the cutoff values.
Figure 3
Figure 3
An ROC plot for our ANN’s training and validation datasets.
Figure 4
Figure 4
The non-cancerous (blue and white strip/dash) and cancerous (solid orange) people in each risk bin (histograms) and the cumulative distribution functions above a certain risk level (lines).
Figure 5
Figure 5
Cancerous (orange) and non-cancerous (blue) people have very different high (solid) and low (dashed) risk trends. Assuming a 1% miss classification rate in the low and high risk categories (black line), we can divide individual cancer risk into 3 categories shown by the shading: high (red), medium (yellow), and low (green).
See this image and copyright information in PMC

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