Benefit of extending near-infrared wavelength range of diffuse reflectance spectroscopy for colorectal cancer detection using machine learning

Abstract Background: Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050–2450 nm wavelength range for noninvasive monitoring of blood glucose. Methods: Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis. Results: Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL). Conclusions: These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050–2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.

Download Full-text

Optimization of tissue classification for colorectal cancer detection using support vector machines and diffuse reflectance spectroscopy

10.1117/12.2615033 ◽

2021 ◽

Author(s):

Marcelo Saito Nogueira ◽

Michael Amissah ◽

Siddra Maryam ◽

Noel Lynch ◽

Shane Killeen ◽

...

Keyword(s):

Colorectal Cancer ◽

Support Vector Machines ◽

Cancer Detection ◽

Diffuse Reflectance ◽

Diffuse Reflectance Spectroscopy ◽

Reflectance Spectroscopy ◽

Support Vector ◽

Tissue Classification ◽

Vector Machines

Download Full-text

Evaluation of wavelength ranges and tissue depth probed by diffuse reflectance spectroscopy for colorectal cancer detection

Scientific Reports ◽

10.1038/s41598-020-79517-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marcelo Saito Nogueira ◽

Siddra Maryam ◽

Michael Amissah ◽

Huihui Lu ◽

Noel Lynch ◽

...

Keyword(s):

Colorectal Cancer ◽

Diffuse Reflectance ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Ex Vivo ◽

Reflectance Spectroscopy ◽

Support Vector ◽

And Performance ◽

Diagnostic Capabilities ◽

Invasive Techniques

AbstractColorectal cancer (CRC) is the third most common type of cancer worldwide and the second most deadly. Recent research efforts have focused on developing non-invasive techniques for CRC detection. In this study, we evaluated the diagnostic capabilities of diffuse reflectance spectroscopy (DRS) for CRC detection by building 6 classification models based on support vector machines (SVMs). Our dataset consists of 2889 diffuse reflectance spectra collected from freshly excised ex vivo tissues of 47 patients over wavelengths ranging from 350 and 1919 nm with source-detector distances of 630-µm and 2500-µm to probe different depths. Quadratic SVMs were used and performance was evaluated using twofold cross-validation on 10 iterations of randomized training and test sets. We achieved (93.5 ± 2.4)% sensitivity, (94.0 ± 1.7)% specificity AUC by probing the superficial colorectal tissue and (96.1 ± 1.8)% sensitivity, (95.7 ± 0.6)% specificity AUC by sampling deeper tissue layers. To the best of our knowledge, this is the first DRS study to investigate the potential of probing deeper tissue layers using larger SDD probes for CRC detection in the luminal wall. The data analysis showed that using a broader spectrum and longer near-infrared wavelengths can improve the diagnostic accuracy of CRC as well as probing deeper tissue layers.

Download Full-text