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Artificial Intelligence in Skin Cancer: Diagnosis and Therapy

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Skin Cancer: Pathogenesis and Diagnosis

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Abstract

Skin cancer is one of the major public health problems worldwide. Its early diagnosis and timely therapy are immensely important in improving patient health. Thus, the improved and accessible diagnostic systems for skin cancer are the most potent determinant of getting the right treatment at the right time. The last two decades have seen unprecedented growth in the application of artificial intelligence (AI) for skin cancer research. Recent advancement in the computational power, digitization of medical imaging, rise of -omics data have accumulated a new opportunity. The ability of AI methods to detect hidden or unknown patterns from such complex datasets reveals their importance. The AI approach in skin cancer has helped to improve the diagnostic and therapeutic strategies, from risk assessment using genomic sequences, accessible smartphone-based “apps” for diagnosis to predict the likelihood of therapy response amidst others. This technology holds a promising potential to automate and assist primary clinicians in improving patient health outcomes through effective diagnostic and therapy strategies using the complex healthcare data paving in the way for precision medicine.

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Abbreviations

2-D:

Two-dimensional

3-D:

Three-dimensional

AI:

Artificial intelligence

AK:

Actinic keratosis

ANN:

Artificial neural network

apps:

Applications

ATAC-seq:

Assay for transposase-accessible chromatin using sequencing

AUC:

Area under the curve

BCC:

Basal cell carcinoma

CADe:

Computer-aided detection

CADx:

Computer-aided diagnosis

cDNA:

Complementary DNA

ChIPseq:

Chromatin immunoprecipitation

CNN:

Convolutional neural network

CT:

Computed tomography

CTCs:

Circulating tumor cells

CUPs:

Cancer of unknown primary

DL :

Deep learning

DNA:

Deoxyribonucleic acid

DNN:

Deep neural network

DTRs:

Dynamic treatment regimes

EHRs:

Electronic health records

EMRs:

Electronic medical records

FDA:

Food and drug administration

GANs:

Generative adversarial networks

GATK:

Genome analysis toolkit

GDL:

Genome deep learning

GWAS:

Genome-wide association studies

H&E:

Hematoxylin and Eosin

HAM10000:

Human Against Machine with 10,000 training images

HLA:

Human leukocyte antigen

IHC:

Immunohistochemistry

ISIC:

International Skin Imaging Collaboration

ISRO:

Indian Space Research Organization

KSC:

Keratinocyte skin cancer

MCC:

Merkel cell carcinoma

MHC:

Major histocompatibility complex

ML:

Machine learning

MRI:

Magnetic resonance imaging

mRNA:

Messenger RNA

MSC:

Melanoma skin cancer

NGS:

Next-generation sequencing

NLP:

Natural language processing

NMSC:

Non-melanoma skin cancer

NNs:

Neural networks

OTR:

Organ transplant recipients

PCA/DA:

Principal component analysis discriminant analysis

PET:

Positron emission tomography

PLS/DA:

Partial least squares discriminant analysis

PPIs:

Protein–protein interactions

PRS:

Polygenic risk score

RNA:

Ribonucleic acid

ROIs:

Region of interests

SCC:

Squamous cell carcinoma

scRNAseq:

Single-cell RNA sequencing

SKCM:

Skin cutaneous melanoma

SNPs:

Single nucleotide polymorphisms

SVMs:

Support vector machines

TA:

Texture analysis

TCGA:

The cancer genome atlas

TD:

Teledermatology

TDD:

Teledermoscopy

TF:

Transcription factor

TMB:

Tumor mutational burden

T-VEC:

Talimogene laherparepvec

UM:

University of Michigan

US:

Ultrasound

UV:

Ultraviolet

WES:

Whole exome sequencing

WHO:

World Health Organization

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Acknowledgement

TD is thankful to UGC (University Grants Commission) for providing fellowship. Authors gratefully acknowledge the computational facility funded by Science and Engineering Research Board (SERB), Government of India (Ref. No.: YSS/2015/000228/LS).

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The author declares no conflict of interest.

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Authors and Affiliations

  1. School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India

    Trishala Das & Andrew M. Lynn

  2. Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, Uttar Pradesh, India

    Vijay Kumar

  3. Amity Institute of Integrative Sciences and Health, Amity University Haryana, Gurgaon, India

    Amresh Prakash

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  1. Trishala Das
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  2. Vijay Kumar
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  3. Amresh Prakash
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  4. Andrew M. Lynn
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Correspondence to Amresh Prakash .

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Editors and Affiliations

  1. Food, Drug & Chemical Toxicology, CSIR-IITR, Lucknow, Uttar Pradesh, India

    Ashish Dwivedi

  2. Food, Drug & Chemical Toxicology, Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India

    Anurag Tripathi

  3. Photobiology Division, Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India

    Ratan Singh Ray

  4. Amity Institute of Neuropsychology and Neurosciences, Amity University, Noida, Uttar Pradesh, India

    Abhishek Kumar Singh

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Das, T., Kumar, V., Prakash, A., Lynn, A.M. (2021). Artificial Intelligence in Skin Cancer: Diagnosis and Therapy. In: Dwivedi, A., Tripathi, A., Ray, R.S., Singh, A.K. (eds) Skin Cancer: Pathogenesis and Diagnosis. Springer, Singapore. https://doi.org/10.1007/978-981-16-0364-8_9

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