The Reflectance Confocal Microscopy – Optical Coherence Tomography (RCM-OCT) device has demonstrated its effectiveness in the in vivo detection and depth assessment of basal cell carcinoma (BCC), though its interpretation can be challenging for novices. Artificial intelligence (AI) has the potential to assist in identifying BCC and measuring its depth in these images. Our goal was to develop an AI model capable of generating 3D volumetric representations of BCC to enhance its detection and depth measurement. We developed AI models trained on OCT images of biopsy-confirmed BCC to detect BCC, generate 3D volumetric representations, and automatically assess tumor depth. These models were then tested on a separate dataset containing images of BCC, benign lesions, and normal skin. The effectiveness of the AI models was evaluated through a blinded reader study and by comparing tumor depth measurements with those obtained from histopathology. The addition of AI-generated 3D renders of BCC improved BCC detection rates, with sensitivity increasing from 73.3% to 86.7% and specificity from 45.5% to 48.5%. A Pearson Correlation coefficient r2 = 0.59 (p=0.02) was achieved in comparing tumor depth measurements between AI -generated renders and histopathology slides. Incorporating AI-generated 3D renders has the potential to improve the diagnosis of BCC and the automated measurement of tumor depth in OCT images, reducing reader dependent variability and standardizing diagnostic accuracy.
In this paper we report the use of a novel multimodal imaging hand-held probe for guiding laser and radiotherapy on nonmelanoma skin cancers (NMSCs) patients. This probe combines the capability of reflectance confocal microscopy (RCM) with that of Optical Coherence Tomography (OCT) to reliably detect cancer markers and measure cancer depth of invasion. These capabilities have shown to be very effective in accurately measuring cancer margins and guiding the therapy.
Melanoma is the most aggressive skin cancer with the highest associated mortality, early diagnosis ensures high survival rates. Currently, in vivo morphological imaging such as reflectance confocal microscopy (RCM) is associated with high sensitivity but moderate specificity. Addition of molecular imaging using PARPi-FL (PARP1-targeted fluorophore) can improve distinction between malignant/potentially malignant lesions. Towards multimodal imaging in vivo, we first investigated differential PARP1 expression in the spectrum of melanocytic lesions. Higher PARP area positivity and intensity were found in melanoma as compared to benign nevi. Thus, PARPi-FL in association with RCM can potentially improve melanoma diagnosis non-invasively in patients.
There is an urgent need for predictive platforms for response to immunotherapy in patients. In vivo phenotyping of tumor-immune microenvironment (TiME) for predicting response to immunotherapy was evaluated using non-invasive reflectance confocal microscopy (RCM) in skin cancer patients. Phenotypes were correlated with underlying biology and response to topical immunotherapy. Using both inflammation and vasculature features, four major phenotypes were observed. The VaschiInfhi phenotype correlated with high immune activation, exhaustion, and vascular signatures while VaschiInflo with endothelial anergy and immune exclusion. Highest response to immunotherapy was seen in VascloInfhi phenotype. This study establishes proof-of-concept for in vivo TiME phenotyping in patients.
Radiation resistance is one of the major causes of recurrence and failure of radiotherapy. Different methods have been used to increase the efficacy of radiation therapy and at the same time restrict the radiation resistivity. From last few years nanoparticles have played a key role in the enhancement of radiosensitization. The densely packed nanoparticles can selectively scatter or absorb the high radiations, which allow better targeting of cellular components within the tumor hence resulting in increased radiation damage to the cancer cells. Glioblastoma multiforme (GBM) is one of the highly radioresistant brain cancer. Current treatment methods are surgical resection followed by concurrent chemo and radiation therapy. In this study we have used in-house engineered gold nano rodes (GNR) and analyzed their effect on U-87MG cell lines. MTT assay was employed to determine the cytotoxic concentration of the nanoparticles. Raman spectroscopy was used to analyze the effect of gold nanoparticles on glioma cells, which was followed by transmission electron microscopic examinations to visualize their cellular penetration. Our data shows that GNR were able to penetrate the cells and induce cytotoxicity at the concentration of 198 μM as determined by MTT assay at 24 post GNP treatment. Additionally, we show that Raman spectroscopy, could classify spectra between untreated and cells treated with nanoparticles. Taken together, this study shows GNR penetration and cytotoxicity in glioma cells thereby providing a rationale to use them in cancer therapeutics. Future studies will be carried out to study the biological activity of the formulation as a radiosensitizer in GBM.
Visual inspection followed by biopsy is the standard procedure for cancer diagnosis. Due to invasive nature of the current diagnostic methods, patients are often non-compliant. Hence, it is necessary to explore less invasive and rapid methods for early detection. Exfoliative cytology is a simple, rapid, and less invasive technique. It is thus well accepted by patients and is suitable for routine applications in population screening programs. Raman spectroscopy (RS) has been increasingly explored for disease diagnosis in the recent past. In vivo RS has previously shown promise in management of both oral and cervix cancers. In vivo applications require on-site instrumentation and stringent experimental conditions. Hence, RS of less invasive samples like exfoliated cells has been explored, as this facilitates collection at multiple screening centers followed by analysis at a centralized facility. In the present study, efficacy of Raman spectroscopy in classification of 15 normal and 29 abnormal oral exfoliated cells specimens and 28 normal and 38 abnormal cervix specimens were explored. Spectra were acquired by Raman microprobe (HE 785, Horiba-Jobin-Yvon, France) from several areas to span the pellet. Spectral acquisition parameters were: microscopic objective: 40X, power: 40 mW, acquisition time: 15 s and average: 3. PCA and PC-LDA of pre-processed spectra was carried out on a 4-model system of normal and tumor of both cervix and oral specimens. Leave-one-out-cross-validation findings indicate ~73 % correct classification. Findings suggest RS of exfoliated cells may serve as a patient-friendly, non-invasive, rapid and objective method for management of cervix and oral cancers.
Oral premalignant lesions (OPLs) such as leukoplakia, erythroplakia, and oral submucous fibrosis, often precede oral cancer. Screening and management of these premalignant conditions can improve prognosis. Raman spectroscopy has previously demonstrated potential in the diagnosis of oral premalignant conditions (in vivo), detected viral infection, and identified cancer in both oral and cervical exfoliated cells (ex vivo). The potential of Raman exfoliative cytology (REC) in identifying premalignant conditions was investigated. Oral exfoliated samples were collected from healthy volunteers (n=20), healthy volunteers with tobacco habits (n=20), and oral premalignant conditions (n=27, OPL) using Cytobrush. Spectra were acquired using Raman microprobe. Spectral acquisition parameters were: λex: 785 nm, laser power: 40 mW, acquisition time: 15 s, and average: 3. Postspectral acquisition, cell pellet was subjected to Pap staining. Multivariate analysis was carried out using principal component analysis and principal component-linear discriminant analysis using both spectra- and patient-wise approaches in three- and two-group models. OPLs could be identified with ∼77% (spectra-wise) and ∼70% (patient-wise) sensitivity in the three-group model while with 86% (spectra-wise) and 83% (patient-wise) in the two-group model. Use of histopathologically confirmed premalignant cases and better sampling devices may help in development of improved standard models and also enhance the sensitivity of the method. Future longitudinal studies can help validate potential of REC in screening and monitoring high-risk populations and prognosis prediction of premalignant lesions.
KEYWORDS: Optical coherence tomography, Reflectivity, Confocal microscopy, Skin, Tumors, In vivo imaging, Spectroscopy, 3D image processing, 3D displays
We present a hand-held implementation and preliminary evaluation of a combined optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) probe for detecting and delineating the margins of basal cell carcinomas (BCCs) in human skin in vivo. A standard OCT approach (spectrometer-based) with a central wavelength of 1310 nm and 0.11 numerical aperture (NA) was combined with a standard RCM approach (830-nm wavelength and 0.9 NA) into a common path hand-held probe. Cross-sectional OCT images and enface RCM images are simultaneously displayed, allowing for three-dimensional microscopic assessment of tumor morphology in real time. Depending on the subtype and depth of the BCC tumor and surrounding skin conditions, OCT and RCM imaging are able to complement each other, the strengths of each helping overcome the limitations of the other. Four representative cases are summarized, out of the 15 investigated in a preliminary pilot study, demonstrating how OCT and RCM imaging may be synergistically combined to more accurately detect BCCs and more completely delineate margins. Our preliminary results highlight the potential benefits of combining the two technologies within a single probe to potentially guide diagnosis as well as treatment of BCCs.
Serum Raman spectroscopy (RS) has previously shown potential in oral cancer diagnosis and recurrence prediction. To evaluate the potential of serum RS in oral cancer screening, premalignant and cancer-specific detection was explored in the present study using 328 subjects belonging to healthy controls, premalignant, disease controls, and oral cancer groups. Spectra were acquired using a Raman microprobe. Spectral findings suggest changes in amino acids, lipids, protein, DNA, and β-carotene across the groups. A patient-wise approach was employed for data analysis using principal component linear discriminant analysis. In the first step, the classification among premalignant, disease control (nonoral cancer), oral cancer, and normal samples was evaluated in binary classification models. Thereafter, two screening-friendly classification approaches were explored to further evaluate the clinical utility of serum RS: a single four-group model and normal versus abnormal followed by determining the type of abnormality model. Results demonstrate the feasibility of premalignant and specific cancer detection. The normal versus abnormal model yields better sensitivity and specificity rates of 64 and 80%; these rates are comparable to standard screening approaches. Prospectively, as the current screening procedure of visual inspection is useful mainly for high-risk populations, serum RS may serve as a useful adjunct for early and specific detection of oral precancers and cancer.
Early detection of oral cancers can substantially improve disease-free survival rates. Ex vivo and in vivo Raman
spectroscopic (RS) studies on oral cancer have demonstrated the applicability of RS in identifying not only malignant and
premalignant conditions but also cancer-field-effects: the earliest events in oral carcinogenesis. RS has also been explored
for cervical exfoliated cells analysis. Exfoliated cells are associated with several advantages like non-invasive sampling,
higher patient compliance, transportation and analysis at a central facility: obviating need for on-site instrumentation. Thus,
oral exfoliative cytology coupled with RS may serve as a useful adjunct for oral cancer screening. In this study, exfoliated
cells from healthy controls with and without tobacco habits, premalignant lesions (leukoplakia and tobacco-pouch-keratosis)
and their contralateral mucosa were collected using a Cytobrush. Cells were harvested by vortexing and centrifugation at
6000 rpm. The cellular yield was ascertained using Neubauer’s chamber. Cell pellets were placed on a CaF2 window and Raman spectra were acquired using a Raman microprobe (40X objective) coupled HE-785 Raman spectrometer.
Approximately 7 spectra were recorded from each pellet, following which pellet was smeared onto a glass slide, fixed in
95% ethanol and subjected to Pap staining for cytological diagnosis (gold standard). Preliminary PC-LDA followed by
leave-one-out cross validation indicate delineation of cells from healthy and all pathological conditions. A tendency of
classification was also seen between cells from contralateral, healthy tobacco and site of premalignant lesions. These results
will be validated by cytological findings, which will serve as the basis for building standard models of each condition.
Oral cancers are the sixth most common malignancy worldwide, with low 5-year disease free survival rates, attributable
to late detection due to lack of reliable screening modalities. Our in vivo Raman spectroscopy studies have demonstrated
classification of normal and tumor as well as cancer field effects (CFE), the earliest events in oral cancers. In view of
limitations such as requirement of on-site instrumentation and stringent experimental conditions of this approach,
feasibility of classification of normal and cancer using serum was explored using 532 nm excitation. In this study, strong
resonance features of β-carotenes, present differentially in normal and pathological conditions, were observed. In the
present study, Raman spectra of sera of 36 buccal mucosa, 33 tongue cancers and 17 healthy subjects were recorded
using Raman microprobe coupled with 40X objective using 785 nm excitation, a known source of excitation for
biomedical applications. To eliminate heterogeneity, average of 3 spectra recorded from each sample was subjected to
PC-LDA followed by leave-one-out-cross-validation. Findings indicate average classification efficiency of ~70% for
normal and cancer. Buccal mucosa and tongue cancer serum could also be classified with an efficiency of ~68%. Of the
two cancers, buccal mucosa cancer and normal could be classified with a higher efficiency. Findings of the study are
quite comparable to that of our earlier study, which suggest that there exist significant differences, other than β-
carotenes, between normal and cancerous samples which can be exploited for the classification. Prospectively, extensive
validation studies will be undertaken to confirm the findings.
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