Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

Aref, Mohamed Hisham; Aboughaleb, Ibrahim H.; El-Sharkawy, Yasser H.

doi:10.1016/j.suronc.2020.10.019

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…HSI is a potential tool in medical diagnostics with many applications in discriminating between cancer and non-cancerous tissues, sensing and detecting the abnormal lesions. We have recently exploit the HIS in developing custom optical imaging system for diagnosis and imaging of ex-vivo breast cancer [ 74 , 75 ], in addition to, using HIS in liver tissue characterization for liver thermal ablation [ 76 ]. However, to the best of our knowledge, this is the first study to use a Hyperspectral camera in producing distinctive spectral signatures based on total RNA extracted from sera samples.…”

Section: Discussionmentioning

confidence: 99%

Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum clinical specimens

Aboughaleb

Matboli

Shawky

et al. 2021

Heliyon

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Hepatocellular carcinoma (HCC) is a major global health problem with about 841,000 new cases and 782,000 deaths annually, due to lacking early biomarker/s, and centralized diagnosis. Transcriptomes research despite its infancy has proved excellence in its implementation in identifying a coherent specific cancer RNAs differential expression. However, results are sometimes overlapped by other cancer types which negatively affecting specificity, plus the high cost of the equipment used. Hyperspectral imaging (HSI) is an advanced tool with unique, spectroscopic features, is an emerging tool that has widely been used in cancer detection. Herein, a pilot study has been performed for HCC diagnosis, by exploiting HIS properties and the analysis of the transcriptome for the development of non-invasive remote HCC sensing. HSI data cube images of the sera extracted total RNA have been analyzed in HCC, normal subject, liver benign tumor, and chronic HCV with cirrhotic/non-cirrhotic liver groups. Data analyses have revealed a specific spectral signature for all groups and can be easily discriminated; at the computed optimum wavelength. Moreover, we have developed a simple setup based on a commercial laser pointer for sample illumination and a Smartphone CCD camera, with HSI consistent data output. We hypothesized that RNA differential expression and its spatial organization/folding are the key players in the obtained spectral signatures. To the best of our knowledge, we are the first to use HSI for sensing cancer based on total RNA in serum, using a Smartphone CCD camera/laser pointer. The proposed biosensor is simple, rapid (2 min), and affordable with specificity and sensitivity of more than 98% and high accuracy.

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Section: Discussionmentioning

confidence: 99%

Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum clinical specimens

Aboughaleb

Matboli

Shawky

et al. 2021

Heliyon

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“…This experimental investigation was a part of series of consecutive research studies in early breast cancer diagnostic. Where our main objective is to select the lowest and highest contrast spectral image to apply our custom subtraction algorithm and delineate the various tumor regions [45][46][47].…”

Section: Methodsmentioning

confidence: 99%

“…The hyperspectral imaging (HSI) system is a substitution modality for biological detection and RGB imaging for its ability to segregate between various materials by utilizing both morphological and spectral characteristics [26][27][28]. This tool has been effectively employed to support the diagnosis of various types of histological samples, for example, blood oxygenation and microcirculation [29][30][31], brain cancer detection [32,32], tissue characterization [33][34][35][36][37][38], and monitoring liver ablation [39][40][41][42][43][44], and in the field of breast cancer [15,[45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Normal and Malignant Breast Tissues utilizing Hyperspectral Images and Associated Differential Spectrum Algorithm

Aref

Youssef

El-Sharkawy

2021

J-BPE

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Breast malignancy is the most pervasive disease and a significant reason for death in women around the world. Recently, Photonic technologies play a vital role in medical applications. This study presents an outline of recent outcomes on the magnitude of breast tissue optical properties. We established an optical system setup utilizing a hyperspectral (HS) camera with poly-chromatic source lights with wavelength (380~1050 nm) for this investigation. Measuring the diffuse reflection (Ŗd) of the investigated ex vivo breast sample to select the optimum spectral image to differentiate between the normal and tumor in the near infra-red and visible (NIR–VIS) spectrum. Finally, applying the custom algorithm to increase the image contrast and applying contour delineation of the malignant regions. The experimental analysis indicates key spectroscopic variations between normal tissue and malignant region in range (550~650 nm). Although, after data normalization, there was noticeable variation at three ranges (630–680 nm), (720–770 nm), and (830–880 nm). The calculated standard deviation (Şd) between the normal and cancer tissue to validate the selective ranges shows that the highest contrast at wavelength 680 nm. However, the histogram analysis illustrates that the spectral image at 600 nm was higher contrast and wavelength 400 nm was the lowest contrast from the select seven-spectral images (400, 500, 600, 700, 800, 900, 1000 nm) to avoid the processing time of the captured HS 128-frames. The proposed potential method could provide promising results on the investigated breast sample optical properties in the diagnostic applications to assist the pathologist and the surgeon. Where the optimum wavelength at 680 nm for diagnostic applications and the ideal spectral image at 600 nm discriminate between the normal and malignant tissue.

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“…46,47 Additionally, a low-cost system was used to differentiate between normal and malignant breast tissue. 48 Another study investigated the variations in photoacoustic response signatures of normal and breast tumour tissue. 49 Additionally, the prospective HSI technique achieves successful results in measuring tissue oxygenation in diagnostic and therapeutic diseases for phlebotomy and non-contact oxygen saturation approaches.…”

Section: Introductionmentioning

confidence: 99%

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

Aref¹,

Hussein²,

Youssef

et al. 2021

J. Spectral Imaging

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Thermal ablation modalities, for example radiofrequency ablation (RFA) and microwave ablation, are intended to prompt controlled tumour removal by raising tissue temperature. However, monitoring the size of the resulting tissue damage during the thermal removal procedures is a challenging task. The objective of this study was to evaluate the observation of RFA on an ex vivo liver sample with both a commercial and a low-cost system to distinguish between the normal and the ablated regions as well as the thermally affected regions. RFA trials were conducted on five different ex vivo normal bovine samples and monitored initially by a custom hyperspectral (HS) camera to measure the diffuse reflectance (Rd) utilising a polychromatic light source (tungsten halogen lamp) within the spectral range 348–950 nm. Next, the light source was replaced with monochromatic LEDs (415, 565 and 660 nm) and a commercial charge-coupled device (CCD) camera was used instead of the HS camera. The system algorithm comprises image enhancement (normalisation and moving average filter) and image segmentation with K-means clustering, combining spectral and spatial information to assess the variable responses to polychromatic light and monochromatic LEDs to highlight the differences in the Rd properties of thermally affected/normal tissue regions. The measured spectral signatures of the various regions, besides the calculation of the standard deviations (δ) between the generated six groups, guided us to select three optimal wavelengths (420, 540 and 660 nm) to discriminate between these various regions. Next, we selected six spectral images to apply the image processing to (at 450, 500, 550, 600, 650 and 700 nm). We noticed that the optimum image is the superimposed spectral images at 550, 600, 650 and 700 nm, which are capable of discriminating between the various regions. Later, we measured Rd with the CCD camera and commercially available monochromatic LED light sources at 415, 565 and 660 nm. Compared to the HS camera results, this system was more capable of identifying the ablated and the thermally affected regions of surface RFA than the side-penetration RFA of the investigated ex vivo liver samples. However, we succeeded in developing a low-cost system that provides satisfactory information to highlight the ablated and thermally affected region to improve the outcome of surgical tumour ablation with much shorter time for image capture and processing compared to the HS system.

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Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

Cited by 14 publications

References 22 publications

Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum clinical specimens

Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum clinical specimens

Characterization of Normal and Malignant Breast Tissues utilizing Hyperspectral Images and Associated Differential Spectrum Algorithm

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

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