Diagnostic Utility of Fluorescence and Diffuse Reflectance Imaging in Gastrointestinal Luminal Pathology

doi:10.21203/rs.3.rs-4969550/v1

BACKGROUND:

Cancer is a fatal disease with significant global mortality. Because cancer is so diverse and lacks biomolecular indicators, it is difficult to diagnose at an early stage using current histopathological diagnostic techniques. However, recent cutting-edge fluorescence and diffuse reflectance (DR) imaging techniques, which use sub-cellular spectral features, have proven beneficial in cancer diagnostics. This study aimed to investigate the diagnostic potential of these methods for discriminating malignant from normal gastrointestinal tissue in an ex vivo setting.

MATERIALS AND METHODS:

An experimental trial was conducted on 42 patients attending the Department of General Surgery, Kasturba Medical College, Manipal. Fluorescence and DR at 399 sites were measured using a handheld multimodal device. The spectral readings were taken immediately after the surgical resection of the specimen. Light-emitting diodes (LED) emitting at 375 nm, 545 nm, 575 nm, and 610 nm wavelengths were used for tissue illumination, and the backscattered light was analyzed using proprietary software. The DR ratio (R610/R545) intensity values were determined for all the different sites examined, and a scatter plot diagram was made to correlate with tissue pathology.

RESULTS:

Spectral readings of histologically confirmed malignant tissues were compared with those of histologically verified normal tissues and were used as controls. In total, 399 sites yielded 766 spectral findings. In an ex vivo context, our study demonstrated a sensitivity of 78.8% and specificity of 98.3% for differentiating between malignant and normal tissues. Additionally, the study showed a 90% specificity and 92% sensitivity for differentiating between benign and malignant tissues.

CONCLUSION:

The diagnostic utility of fluorescence and DR imaging demonstrates its potential to discriminate between normal tissues and pathological lesions of the gastrointestinal tract in real-time.

Fluorescence Spectroscopy

Endoscopy

Gastrointestinal Luminal Diseases

Diffuse Reflectance Spectroscopy

Gastrointestinal conditions lead to significant medical use, financial burden, and distress worldwide. In 2019, digestive diseases caused 88.99 million Disability Adjusted Life Years (DALY) worldwide (3.51% of all DALYs) and were ranked as the 13th most common cause [1]. According to Global Cancer Statistics (GLOBOCAN) 2020, colorectal cancer is the third most common cancer and second leading cause of cancer-related deaths. More than 1.9 million new colorectal cancer cases and 935,000 deaths were estimated to occur in 2020, representing approximately one in 10 cancer cases and deaths. Its incidence is expected to increase by 20% by 2030. Gastric cancer is the fifth most common cancer and the fourth leading cause of cancer-related deaths worldwide, accounting for 1.089 million new cancer cases and 0.769 million deaths in 2020, despite the global decline in incidence and mortality over the past several decades [2]. Several factors, including lifestyle choices, physical inactivity, stress, and heredity, have been associated with the increasing frequency noted in certain regions.

Conventional endoscopy is the gold standard diagnostic modality for both upper and lower gastrointestinal tract diseases. Visualization using white light is the foundation of the flexible conventional endoscopic technique, which permits the surgeon to visualize the luminal parts of the gastrointestinal tract and obtain tissue for histopathological examination.

Most gastrointestinal cancers originate from epithelium. Owing to morphological and metabolic alterations, epithelial tissues change their optical properties as cancer progresses. These subtle early changes can be detected using spectroscopic techniques based on fluorescence and diffuse reflectance. Because spectroscopic techniques rely on light-tissue interactions, they can identify lesions on the tissue surface that are much smaller and much earlier than macroscopically noticeable. Such changes occurring at the level of sub-cellular components begin much earlier than microscopic cellular changes that a pathologist relies on to make a histopathological diagnosis [4]. The process of obtaining a diagnosis by histopathological means relies on microscopic cellular level changes that not only require fixing the specimen and staining the tissue using hematoxylin and eosin but also require a trained pathologist to interpret the microscopic features.

This process takes time and is well-suited for elective situations. There has been a constant endeavor by surgeons as well as basic scientists to develop alternate methods, particularly those that offer the potential for real-time diagnosis. In this context, the use of fluorescence technology has made significant contributions and has many exciting prospects for translation into clinical utility [3].

One of the many exciting point-based spectroscopic methods is diffuse reflectance spectroscopy (DRS), which is inexpensive and has a proven clinical utility. DRS is simpler to use than complex micro-endoscopic probes because it does not require lasers or highly developed magnification lenses. By quantifying otherwise undetectable cellular and tissue alterations that occur throughout the early and late phases of malignant transformation at the nano and microscale levels, spectroscopy-based techniques can be used to distinguish between distinct tissue classes. Such studies rely on variations in the spectral emission wavelength and intensity as well as on the absorption and scattering properties of tissue constituents interacting with light. This makes it possible to identify and quantify different chromophores in the tissue, such as collagen, water, lipids, oxygenated and deoxygenated hemoglobin, and β-carotene. Furthermore, information about the underlying cellular architecture can be obtained from the scattering coefficients [5].

The present study examined the diagnostic utility of diffuse reflectance and fluorescence imaging in differentiating between gastrointestinal luminal mucosal pathologies in the context of their ability to distinguish between cancerous and normal gastrointestinal tissues in an ex vivo setting. A multispectral imaging camera has been utilized to detect cancer in the luminal mucosa of resected segments of the intestine and in the margins of excised intestinal tissue segments using fluorescence and changes in oxygenated hemoglobin absorption. The clinical study documented the fluorescence and DR spectral images of luminal, serosal, and lymph nodal tissue of benign and malignant gastrointestinal tract lesions and assessed the diagnostic accuracy of the device for the detection of malignancies by correlating imaging data with the final histopathological results.

A clinical study was conducted at the Department of General Surgery, Kasturba Medical College and Hospital, Manipal, between October 2022 and September 2022. The study commenced after obtaining approval from the Institutional Review Board (625/2020) and was registered with the Clinical Trial Registry of India (CTRI/2021/01/030415).

All patients suspected of having gastrointestinal luminal pathology undergoing surgical resection for the diseased segment of the gastrointestinal tract and willing to consent to the study were included. However, patients under 18 years of age and those with non-luminal gastrointestinal pathology were excluded from the study.

Methodology

The consenting participants underwent a standard of care resectional surgery based on the surgeon’s pre-operative evaluation and diagnosis. The resected specimen was handed over to the investigator in the operating room as soon as the surgical procedure was completed. Each specimen was cleaned with water to remove blood and luminal debris. The samples were then oriented with proper labeling and digital photographs were obtained for future reference. The multispectral imaging device was calibrated before recording every patient specimen to the light conditions of the room using the tissue phantom provided. Four monochrome images were captured sequentially by the device under illumination with the four LED light sources, from the most representative predetermined mucosal and serosal areas of the specimen. Each image was captured at a fixed distance from the tumor. Selected lymph nodes were also harvested and imaged as described above. The extracted lymph nodes were histologically examined after individual labeling. Doughnuts and re-excised specimens in procedures using staplers were also photographed. For future reference and identification, corresponding digital colour photographs of the tissues imaged for fluorescence were also gathered and recorded. After the spectral recordings were completed, the samples were immersed in formalin and sent to the laboratory for histological examination.

The proprietary software marks the region of interest based on the pseudo color fluorescence image and DR image R610/R545 ratio values. The software then identified the highest ratio value in the region marked by the researcher.

A pathologist blinded to spectroscopic data evaluated the samples to confirm the presence of malignant and normal tissues using traditional haematoxylin/eosin (H&E) staining. The ratio of R610/R545 was correlated with the histopathology results and presented as a scatter plot diagram.

Specifications of the device

The multispectral imaging device was developed by Sascan Meditech for the bimodal imaging of epithelial tissues. LEDs were positioned in circles around a monochrome high-resolution (5 MPx) camera connected to a zoom lens. A set of ultraviolet-A (375 nm) emitting LEDs was used to examine collagen fluorescence, whereas a set of green (545 nm), yellow (575 nm), and red (610 nm) wavelength LEDs were used to examine the scattering properties of tissue and changes in oxy and deoxy absorption. A customized filter was used to transmit tissue autofluorescence and elastically back-scattered light at 542, 577, and 610 nm into the sensor while preventing excitation light at 375 nm from entering the detection system. Crossed polarizers were used in the optical path to avoid specular reflections from the tissue surface from entering the sensor. The camera was connected to the USB port of a tablet or laptop running 64-bit Windows 10, and image capture and analysis were performed using proprietary software installed on the laptop (Fig. 1A) [6].

The device is focused on the tissue, and multispectral images are captured sequentially using the capture button on software or hardware. The region of interest (RoI) is marked on all images based on the abnormality seen in the processed pseudo-color map (PCM) of the tissue fluorescence and DR ratio images. The software checked the ratio values (R610/R545) and marked the site with the highest DR ratio value in the RoI.

Statistical analysis

A scatter plot diagram of the DR image intensity ratio R610/545 values for different sites was constructed. Cut-offs were drawn at the average ratio values to distinguish normal sites from malignant lesions, normal sites from benign lesions, and malignant lesions from benign lesions (Figs. 2,3,4).

The Z-test ensured that the two groups' mean values were identical. The null hypothesis was that both means were equal. The alternative hypothesis was that the mean number of abnormal sites was more significant than that of normal sites. The null hypothesis was to be rejected if, at a 99% confidence level, the z statistic was less than the z critical value. We have described the qualitative data in terms of the numbers and percentages. Statistical significance was set at P < 0.05.

In classifying normal from malignant, the mean pixel value of the R610/545 image ratio for normal was 1.945, and for malignant, it was 5.032. In the scatter plot of the 199 normal sites, 2 were misclassified as malignant. The table shows the sensitivity, specificity, PPV, and NPV for each pair.

Software

Proprietary software was developed for this study. The software triggers the LEDs in sequential order, captures images, processes the recorded images in real-time, and provides ratio values to determine the tissue status.

The software's home screen has a window with a list of the patients recorded and an option for registering new patients where we can enter patient details such as Unique ID (Fig. 1B). The camera can be operated by the screen menu, followed by calibration of the camera and LEDs with the working dim light conditions. Once the device was calibrated to ambient light conditions, the probe was placed in position and focused for image clarity.

The capture button on the software or the hardware switch on the probe was used to trigger the LEDs and capture the images. At least two separate sites are imaged for each abnormal lesion (depicted as green on the serosal surface and yellow on the mucosal lesion in Figs. 5 and 7), and the site farthest away from the abnormal lesion (depicted as blue on the mucosal surface in Figs. 5 and 7) is chosen to represent the normal area in that resected specimen.

The captured images were processed in the software, and the region of interest (ROI) was marked based on the fluorescence emission pseudo-color image F375 (Figs. 6 and 8). After marking the ROI, the software prompts the selection of whether the marked region is normal or abnormal. The built-in analysis will provide a ratio value corresponding to the changes in oxygenated hemoglobin absorption. Each site's R610/545 ratio value was analyzed to distinguish normal from abnormal tissues.

A total of 42 patients who attended the Department of General Surgery during the study period and satisfied the eligibility criteria were included in the study. Among the above 42 patients, 28 (66.7%) were male. Among the included patients, 22 (52.4%) were aged between 51-70 years, followed by 11 (26.2%) were below 50 years, and the rest were above 70 years. In 23/42 (54.8%) had presented with comorbidities and gastrointestinal disease. In 27 (35.7%) had malignant pathology confirmed by a histopathological examination, while the remaining patients had benign pathology.

Histopathologic Analysis:

The results are presented in Tables 1 and 2. Table 3 lists the confusion matrix for comparing malignant and normal tissues.

Table 1: Histopathological confirmation of malignant and benign tissue type

HPE Impression

Frequency of observation

Percentage

Benign

(Ischaemic mucosal injury, multifocal intestinal ulceration, ulcerative colitis, lipoma)

15

35.7%

Malignant

(Adenocarcinoma, neuroendocrine tumour, diffuse large cell lymphoma)

27

64.3%

Table 2: Histopathological confirmation of proximal, distal, and serosal involvement in specimens with malignant lesions

	HPE Impression	Frequency of observation	Percentage
Proximal margin	Free of tumor cells	26	96.2%
Proximal margin	Diffuse enterochromaffin-like-cell hyperplasia	1	3.7%
Distal margin	Free of tumor cells	26	96.2%
Distal margin	Positive for malignancy	1	3.7%
Serosal involvement	Free of tumor cells	13	48%
Serosal involvement	Positive for malignancy	14	52%

At a 99% confidence interval, the right-tailed Z test was carried out with the following hypotheses: Ho: null hypothesis: mean malignant=mean normal and H1: alternative hypothesis: mean malignant> mean normal.

Table 3: Confusion matrix comparing malignant tissue versus normal tissue. [PPV: Positive predictive value; NPV: Negative predictive value; Z cal: Z calculated]

Tissue type

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

Mean malignant

Mean normal

Count malignant

Count normal

Z cal

Zp

Malignant tissue vs normal tissue

78.8

98.8

98.3

84.1

5.032

(malignant)

1.94

(normal)

190

199

20.82

2.57

Zcal >Zp; Null hypothesis rejected

Spectral imaging and analysis of malignant tissue:

Spectral imaging and analysis of benign tissue:

Using a scatter plot of DR ratio R610/545 values for different sites, we assessed the diagnostic utility of DRS and fluorescence in differentiating between benign and malignant pathologies of the gastrointestinal system (Figure 4).

At a 99% confidence interval, the right-tailed Z test was carried out with the following hypothesis: Ho: null hypothesis: mean normal = mean benign and H1: alternative hypothesis: mean normal > mean benign (Table 4).

Table 4: Confusion matrix comparing malignant tissue versus benign tissue. [PPV: Positive predictive value; NPV: Negative predictive value; Z cal: Z calculated]

Tissue type

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

Mean malignant

Mean normal

Count malignant

Count normal

Z cal

Zp

Malignant tissue vs. Benign tissue

92

90

90.2

91.8

5.1 (malignant)

2.32 (benign)

100 (malignant)

100

(benign)

12.85

2.57

Zcal>Zp;

Null hypothesis rejected

A scatterplot comparing the DR ratio values (R610/R545) for benign tissues with the pathological results is shown in Figure 3. At a 99% confidence interval, the right-tailed Z test was carried out with the following hypothesis: Ho: null hypothesis: mean normal = mean benign and H1: alternative hypothesis: mean normal> mean benign (Table 5).

Table 5: Confusion matrix comparing normal tissue versus benign tissue. [PPV: Positive predictive value; NPV: Negative predictive value; Z cal: Z calculated]

Tissue type

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

Mean malignant

Mean normal

Count malignant

Count normal

Z cal

Zp

Benign tissue vs. Normal tissue

39

74.41

63.94

51.2

2.39 (benign)

2.06 (normal)

100

96

2.24

2.57

Zcal<Zp;

Null hypothesis accepted

Gastrointestinal luminal pathologies can be challenging to diagnose, particularly when encountered during emergency surgery when laboratory support services for histopathological evaluation may not be available. Real-time decision-making by the surgeon is fraught with the possibility of a wrong diagnosis, resulting in suboptimal surgical intervention and a poorer outcome. A technology and device that aids in the real-time diagnosis of luminal pathologies will be a welcome addition to the armamentarium available to the surgeon for on-table operative decisions.

In the current study, we employed 375, 545, 575, and 610 nm DRS and fluorescence imaging to distinguish between normal and malignant tissues. The R610/R545 ratio obtained from the sampled site was used to differentiate between the normal and diseased tissues. This study used histologically proven normal tissues as controls, and the DR ratio values (R610/R545) were compared with those of histologically proven malignant tissue. The DR ratio values from 389 locations, including 190 malignant and 199 normal sites, were obtained. In an ex vivo context, our study demonstrated a sensitivity of 78.8% and specificity of 98.3% for differentiating between malignant and normal tissues. Similarly, a sensitivity of 50% and specificity of 74.4% were obtained for distinguishing normal and benign tissues. An overall sensitivity of 92% and specificity of 90% were observed in differentiating malignant from benign lesions. In the scatter plot of the 199 normal sites, 2 were misclassified as malignant. This misclassification may be due to subcellular changes that are so subtle and early, to be detected by spectroscopy, but not yet manifested to change the cellular morphology for the pathologist to report it as malignant. There is also the possibility that the pathologist may have underreported the findings. In addition, ischemic changes may have altered the optical characteristics, resulting in misclassification.

DRS is useful in various contexts for the noninvasive diagnosis of gastrointestinal tract diseases. The method presented here for identifying hypoxic regions is predicated on Hb–oxygen dynamics and can supplement the endoscopic identification of abnormalities in the gastrointestinal mucosa. Red blood cells in arteries contain most of the hemoglobin in the blood, and the measured mucosal SpO2 indicates capillary oxygenation in both hypoxic and ischemic tissues. It is well established that arterial SpO₂ and mucosal SpO₂ differ. Furthermore, much research has already been conducted on the connection between blood flow, hypoxia, and malignant tumors, and this association should help provide valid diagnostic support. The benefit of using the information obtained from this spectral image analysis is its ability to track and visualize changes associated with local occurrences and their surroundings. To increase the accuracy of spectral analysis, techniques such as multivariate simultaneous analysis and broad wavelength range absorption spectrum evaluation have been employed in addition to the narrow-band wavelength filter method for measuring absorbance. The multivariate optimization calculation approach has been made possible by advancements in digitalization, data transfer, and computer software, in addition to the contributions of endoscopic and spectroscopic instruments [7, 8].

Using 1,273 measured spectra from 21 colonic carcinomas, epiploic fat, and normal colonic mucosal specimens, Langhout et al. derived the spectral characteristics with a 95% sensitivity and 88% specificity, using a combination of DRS and Fluorescence and were able to differentiate the tumor from the surrounding tissue. Significant spectrum abnormalities were observed in the shift from healthy tissue to malignancies. Out of the 1,273 spectroscopic measurements, 670 were measured as portions of line measurements and 603 spectra measurements, of which 276 spectra were on the malignant tissue, 122 spectra on the colon mucosa, 130 spectra on the epiploic fat, and 75 spectra on the mesocolon. With a sensitivity and specificity of 95% and 88%, respectively, the overall accuracy of distinguishing cancerous tissue from healthy tissue was 91% using the DRS and fluoroscopic features [9].

Ehlen et al. examined the fluorescence emission spectra (473 nm) and DRS of normal and tumor tissues from resected colorectal cancer specimens. Fiber optical probes were used to record the spectra in an ex vivo setting, and multivariate analysis was performed on the results. When distinguishing benign from malignant tissues, the combination of the two methods provided the highest sensitivity (98%), specificity (93%), and accuracy (96%) [10].

A study by Dhar et al. on the in-vivo colonic tissues of 45 patients using 300–800 nm and 350–800 nm wavelengths captured 483 spectral images. Sensitivity and specificity of 80% and 86% were demonstrated in distinguishing malignant lesions from normal tissues, and sensitivity and specificity of 92% and 82% were observed in distinguishing all pathological lesions from normal tissues [11].

A total of 1363 spectral images were obtained by Nogoueira et al., utilizing 400–440 nm and 540–580 nm wavelengths on ex vivo colonic and rectal tissues of 47 individuals. They demonstrated a sensitivity of 84.2% and a specificity of 86.3% in discriminating between malignant lesions and normal tissues [12].

The present study was able to distinguish between normal and malignant tissues and locate the most malignant site in the dissected sample using DRS in combination with fluorescence on illumination of the tissue with LEDs emitting at 375 nm, 545 nm, 575 nm, and 610 nm. Fluorescence and DRS may prove beneficial for analyzing and evaluating cancer-free margin excision in gastrointestinal luminal malignancies. The most relevant use of the technique would be in the surgery room when it is necessary to examine the resection margin for cancerous tissue in real-time, such as an area at risk or a tumor that may not have been completely removed. This might lower the likelihood of a positive resection margin and, in turn, reduce the possibility of cancer recurrence in patients with luminal cancers of the gastrointestinal tract.

Ethics Approval and consent to participate: Kasturba Medical College and Kasturba Hospital Institutional Ethics Committee – 625/2020

Clinical Trial Registry – INDIA: CTRI/2021/01/030415 [Registered on: 12/01/2021]

Consent for publication: Consent was obtained from all research participants before publication.

Availability of data and material: The data supporting this study's findings are available upon request from the corresponding author (AR). The data are not publicly available because they contain information that could compromise the privacy of research participants.

Acknowledgment: Department of General Surgery and Department of Surgical Oncology, Kasturba Medical College, Manipal, for permitting recording from surgical resected specimens

Conflicts of interest: The authors declare no conflicts of interest.

Author Contributions: JA and SM conceptualized and designed the study and collected and organized the data. JA, SN, PG, SP, RS, and SM analyzed and interpreted the data and prepared the manuscript. AR reviewed and edited the manuscript and handled correspondence with the journal.

Funding: This study did not receive any specific grants from public, commercial, or not-for-profit funding agencies.

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No competing interests reported.

Diagnostic Utility of Fluorescence and Diffuse Reflectance Imaging in Gastrointestinal Luminal Pathology

Status:

Version 1

Abstract

BACKGROUND:

MATERIALS AND METHODS:

RESULTS:

CONCLUSION:

Figures

INTRODUCTION

MATERIALS AND METHODS

RESULTS

DISCUSSION

CONCLUSION

Declarations

References

Additional Declarations

Status:

Version 1