Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics

Kakileti, Siva Teja; Madhu, Himanshu; Manjunath, Geetha; Wee, Leonard; Dekker, André; Sampangi, Sudhakar

doi:10.1016/j.artmed.2020.101854

Cited by 54 publications

(23 citation statements)

References 23 publications

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“…Various international radiologic organizations, such as the American College of Radiology (ACR), and the Canadian, Indian and European associations of radiologists, are progressively and efficiently integrating AI techniques. [30][31][32]…”

Section: Discussionmentioning

confidence: 99%

“…Once trained, CNNs can classify the microcalcifications as either benign or malignant on images it has not seen before. 3,8,32,35 The automatic hierarchical feature learning ability of deep CNNs also helps reduce false-positive rates in breast cancer screening. 36 However, owing to the small sample size and variable presentation, it is a herculean task to train the CNNs from scratch for the medical images.…”

Section: Current Trends and Techniques Used For Ai In Imagingmentioning

confidence: 99%

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An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Goyal¹

2021

RMI

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This review article aims to discuss current trends, techniques, and promising uses of artificial intelligence (AI) in breast imaging, apart from the pitfalls that may hinder its progress. It includes only the commonly used and basic terminology imperative for physicians to know. AI is not just a computerized approach but an interface between humans and machines. Apart from reducing workload and improved diagnostic accuracy, radiologists get more time for patient care or clinical work by using various machine learning techniques that augment their productivity. Inadequate data input with suboptimal pattern recognition, data extraction challenges, legal implications, and exorbitant costs are a few pitfalls that AI algorithms still face while analyzing and giving appropriate outcomes. Various machine learning approaches are used to construct prediction models for clinical decision support and ameliorating patient management. Since AI is still in its fledgling state, with many limitations for clinical implementation, clinical support and feedback are needed to avoid algorithmic errors. Hence, both machine learning and human insight complement each other in revolutionizing breast imaging.

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Section: Discussionmentioning

confidence: 99%

Section: Current Trends and Techniques Used For Ai In Imagingmentioning

confidence: 99%

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Goyal¹

2021

RMI

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“…Figures 1A and 1B show the analyzed images with lesion marking and vascularity, respectively. Additional details of the Thermalytix algorithms are described in our recent technical article 30 …”

Section: Methodsmentioning

confidence: 99%

Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women

Kakileti¹,

Madhu²,

Krishnan³

et al. 2020

JCO Global Oncology

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PURPOSE To evaluate the sensitivity and specificity of Thermalytix, an artificial intelligence–based computer-aided diagnostics (CADx) engine, to detect breast malignancy by comparing the CADx output with the final diagnosis derived using standard screening modalities. METHODS This multisite observational study included 470 symptomatic and asymptomatic women who presented for a breast health checkup in two centers. Among them, 238 women had symptoms such as breast lump, nipple discharge, or breast pain, and the rest were asymptomatic. All participants underwent a Thermalytix test and one or more standard-of-care tests for breast cancer screening, as recommended by the radiologists. Results from Thermalytix and standard modalities were obtained independently in a blinded fashion for comparison. The ground truth used for analysis (normal or malignant) was the final impression of an expert clinician based on the symptoms and the available reports of standard modalities (mammography, ultrasonography, elastography, biopsy, fine-needle aspiration cytology, and so on). RESULTS For the 470 women, Thermalytix resulted in a sensitivity of 91.02% (symptomatic, 89.85%; asymptomatic, 100%) and specificity of 82.39% (symptomatic, 69.04%; asymptomatic, 92.41%) in detection of breast malignancy. Thermalytix showed an overall area under the curve (AUC) of 0.90, with an AUC of 0.82 for symptomatic and 0.98 for asymptomatic women. CONCLUSION High sensitivity and high AUC of Thermalytix in women of all age groups demonstrates the efficacy of the tool for breast cancer screening. Thermalytix, with its automated scoring and image annotations of potential malignancies and vascularity, can assist the clinician in better decision making and improve quality of care in an affordable and radiation-free manner. Thus, we believe Thermalytix is poised to be a promising modality for breast cancer screening.

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“…However, machine-learning approaches offer an opportunity to reduce the extent of and need for manual expertise. The concept of using radiomics-based thermal image biomarkers and machine learning as a noninvasive tool for breast cancer screening has recently been investigated (Kakileti et al, 2020). In this approach, thermal images are quantitatively analyzed to objectively score the risk of breast cancer through a machine-learning approach.…”

Section: Short Communication Physiologymentioning

confidence: 99%

Delta thermal radiomics: An application in dairy cow teats

et al. 2022

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Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics

Cited by 54 publications

References 23 publications

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women

Delta thermal radiomics: An application in dairy cow teats

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