Line-field confocal optical coherence tomography in dermato-oncology: A literature review towards harmonized histopathology-integrated terminology.

Non-invasive diagnostics like line-field confocal optical coherence tomography (LC-OCT) are being implemented in dermato-oncology. However, unification of terminology in LC-OCT is lacking. By reviewing the LC-OCT literature in the field of dermato-oncology, this study aimed to develop a unified terminological glossary integrated with traditional histopathology. A PRISMA-guided literature-search was conducted for English-language publications on LC-OCT of actinic keratosis (AK), keratinocyte carcinoma (KC), and malignant melanoma (MM). Study characteristics and terminology were compiled. To harmonize LC-OCT terminology and integrate with histopathology, synonymous terms for image features of AK, KC, and MM were merged by two authors, organized by skin layer and lesion-type. A subset of key LC-OCT image-markers with histopathological correlates that in combination were typical of AK, squamous cell carcinoma in situ (SCCis), invasive squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and MM in traditional histopathology, were selected from the glossary by an experienced dermatopathologist. Seventeen observational studies of AK (7 studies), KC (13 studies), MM (7 studies) utilizing LC-OCT were included, with 117 terms describing either AK, KC, or MM. These were merged to produce 45 merged-terms (61.5% reduction); 5 assigned to the stratum corneum (SC), 23 to the viable epidermis, 2 to dermo-epidermal junction (DEJ) and 15 to the dermis. For each lesion, mandatory key image-markers were a well-defined DEJ and presence of mild/moderate but not severe epidermal dysplasia for AK, severe epidermal dysplasia and well-defined DEJ for SCCis, interrupted DEJ and/or dermal broad infiltrative strands for invasive SCC, dermal lobules connected and/or unconnected to the epidermis for BCC, as well as single atypical melanocytes and/or nest of atypical melanocytes in the epidermis or dermis for MM. This review compiles evidence on LC-OCT in dermato-oncology, providing a harmonized histopathology-integrated terminology and key image-markers for each lesion. Further evaluation is required to determine the clinical value of these findings.

The combination of dermoscopy and reflectance confocal microscopy increases the diagnostic confidence of amelanotic/hypomelanotic lentigo maligna.

The dermoscopic diagnosis of amelanotic/hypomelanotic lentigo maligna/lentigo maligna melanoma (AHLM/LMM) may be very difficult in its early stages because of lack of pigment. Reflectance confocal microscopy (RCM) is an imaging technique that is especially helpful for the diagnosis of lentigo maligna. To determine the diagnostic performances of dermoscopy and RCM in the diagnosis of AHLM/LMMs we evaluated dermoscopic and RCM images of consecutive cases of histopathologically confirmed AHLM/LMMs, amelanotic/hypomelanotic basal cell carcinoma and squamous cell carcinoma (AHBCCs/AHSCCs), amelanotic/hypomelanotic benign lesions (AHBLs), and actinic keratoses (AKs) from five participating centers. Sensitivity, specificity, accuracy, predictive values, and level of diagnosis confidence were calculated for both diagnostic procedures. Both dermoscopy and RCM showed diagnostic performance >97% in the diagnosis of AHLM/LMMs versus AHBCC/AHSCCs and their combination slightly improved diagnostic performance, with accuracy increasing from 98.0% to 99.1%. Similarly, RCM in combination with dermoscopy showed a tiny increase in the diagnostic performance in the diagnosis of AHLM/LMMs versus AHBLs (accuracy increased from 87.2% to 88.8%) and versus AKs (accuracy increased from 91.4% to 93.4%). Although the increase in diagnostic performance due to RCM was modest, the combination of dermoscopy and RCM greatly increased the level of confidence; high confidence in the diagnosis of AHLM/LMMs versus AHBLs increased from 36.2% with dermoscopy alone to 76.6% with dermoscopy plus RMC. Based on our results, dermoscopy and RCM should be complementary to improve not only diagnostic accuracy but also the level of diagnostic certainty in the diagnosis of AHLM/LMMs.

Dynamic optical coherence tomography evaluation in locally advanced basal cell carcinoma during sonidegib treatment.

BACKGROUND: Basal cell carcinoma (BCC) is the most common cancer in the Caucasian population. It has a multifactorial pathogenesis, in which constitutive activation of the Sonic Hedgehog signalling (SHH) pathway (via mutations in PTCH1 or SMO genes) represents by far the most common genetic aberration. The introduction of vismodegib and sonidegib, two SHH pathway inhibitors, changed the therapeutic approach of locally advanced and metastatic BCCs. EADO's (European Association of Dermato-Oncology) new staging system refers to these as 'difficult-to-treat' BCCs. OBJECTIVE: The aim was to evaluate sonidegib's effectiveness in patients affected by difficult-to-treat BCCs by using non-invasive diagnostic techniques. METHODS: We retrospectively evaluated 14 patients (4 females, 10 males; mean age 77 ± 11 years) affected by difficult-to-treat BCCs treated with oral sonidegib 200 mg/day that were followed with total body videodermoscopy (V-Track, Vidix 4.0) and dynamic optical coherence tomography (D-OCT, VivoSight Dx) since May 2022. Considering the risk of rhabdomyolysis routine blood tests, especially for creatine kinase concentrations, were performed. All treated patients were inserted in the BasoCare database, which aims to offer support to patients taking sonidegib. Complete and partial responses were evaluated by the overall reduction of the number of lesions and their individual sizes. Safety was evaluated by assessing the occurrence and severity of adverse reactions. RESULTS: Eighty per cent achieved complete clearance and 75% reduction of diameter. D-OCT scans performed at every follow-up showed concordance with clinical appearance and demonstrated reduction of hyporeflective structures, that is, islets of tumour cells and overall improvement of morphology. CONCLUSION: Sonidegib can be considered an effective treatment option in cases where surgery or radiotherapy would be unfeasible or has previously failed, although pigmented lesions did not show complete clearance, suggesting that there are factors other than the SHH pathway involved in tumour growth. Videodermoscopy and D-OCT were useful in the quick and seamless follow-up of lesions and added valuable information in assessing efficacy.

Dermatoscopy and reflectance confocal microscopy for basal cell carcinoma diagnosis and diagnosis prediction score: A prospective and multicenter study on 1005 lesions.

BACKGROUND: Basal cell carcinoma (BCC) is usually diagnosed by clinical and dermatoscopy examination, but diagnostic accuracy may be suboptimal. Reflectance confocal microscopy (RCM) imaging increases skin cancer diagnostic accuracy. OBJECTIVE: To evaluate additional benefit in diagnostic accuracy of handheld RCM in a prospective controlled clinical setting. METHODS: A prospective, multicenter study in 3 skin cancer reference centers in Italy enrolling consecutive lesions with clinical-dermatoscopic suspicion of BCC (ClinicalTrials.gov: NCT04789421). RESULTS: A total of 1005 lesions were included, of which 474 histopathologically confirmed versus 531 diagnosed by clinical-dermatoscopic-RCM correlation, confirmed with 2 years of follow-up. Specifically, 740 were confirmed BCCs. Sensitivity and specificity for dermatoscopy alone was 93.2% (95% CI, 91.2-94.9) and 51.7% (95% CI, 45.5-57.9); positive predictive value was 84.4 (95% CI, 81.7-86.8) and negative predictive value 73.3 (95% CI, 66.3-79.5). Adjunctive RCM reported higher rates: 97.8 (95% CI, 96.5-98.8) sensitivity and 86.8 (95% CI, 82.1-90.6) specificity, with positive predictive value of 95.4 (95% CI, 93.6-96.8) and negative predictive value 93.5 (95% CI, 89.7-96.2). LIMITATIONS: Study conducted in a single country. CONCLUSIONS: Adjunctive handheld RCM assessment of lesions clinically suspicious for BCC permits higher diagnostic accuracy with minimal false negative lesions.

Detection and subtyping of basal cell carcinoma in whole-slide histopathology using weakly-supervised learning.

The frequency of basal cell carcinoma (BCC) cases is putting an increasing strain on dermatopathologists. BCC is the most common type of skin cancer, and its incidence is increasing rapidly worldwide. AI can play a significant role in reducing the time and effort required for BCC diagnostics and thus improve the overall efficiency of the process. To train such an AI system in a fully-supervised fashion however, would require a large amount of pixel-level annotation by already strained dermatopathologists. Therefore, in this study, our primary objective was to develop a weakly-supervised for the identification of basal cell carcinoma (BCC) and the stratification of BCC into low-risk and high-risk categories within histopathology whole-slide images (WSI). We compared Clustering-constrained Attention Multiple instance learning (CLAM) with StreamingCLAM and hypothesized that the latter would be the superior approach. A total of 5147 images were used to train and validate the models, which were subsequently tested on an internal set of 949 images and an external set of 183 images. The labels for training were automatically extracted from free-text pathology reports using a rule-based approach. All data has been made available through the COBRA dataset. The results showed that both the CLAM and StreamingCLAM models achieved high performance for the detection of BCC, with an area under the ROC curve (AUC) of 0.994 and 0.997, respectively, on the internal test set and 0.983 and 0.993 on the external dataset. Furthermore, the models performed well on risk stratification, with AUC values of 0.912 and 0.931, respectively, on the internal set, and 0.851 and 0.883 on the external set. In every single metric the StreamingCLAM model outperformed the CLAM model or is on par. The performance of both models was comparable to that of two pathologists who scored 240 BCC positive slides. Additionally, in the public test set, StreamingCLAM demonstrated a comparable AUC of 0.958, markedly superior to CLAM's 0.803. This difference was statistically significant and emphasized the strength and better adaptability of the StreamingCLAM approach.

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

BACKGROUND: Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity. OBJECTIVES: To develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high-risk (HR) and low-risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors. METHODS: The deep learning systems were trained on 161 H&E -stained sections which contained 51 sections of HR-BCC, 50 sections of LR-BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi-head self-attention (MSA) U-Net, nuclei segmentation by HoVer-Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers. RESULTS: MSA-U-Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977 ± 0.0159, 0.955 ± 0.0181, 0.885 ± 0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T-T_meanEdgeLength, T-T_Nsubgraph, S-T_HarmonicCentrality, S-S_Degrees. The risk stratification model can well predict HR-BCC and LR-BCC with 0.920 ± 0.0579, 0.839 ± 0.0176, 0.825 ± 0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR-BCC and LR-BCC comprised IntensityMin, Solidity, T-T_minEdgeLength, T-T_Coreness, T-T_Degrees, T-T_Betweenness, S-T_Degrees. CONCLUSIONS: This framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.

Line-field confocal optical coherence tomography of eyelid margin growths: A case series.

BACKGROUND: The clinical differential diagnosis of lesions arising on the eyelid margin may be challenging and an unneeded surgical approach may have serious functional and aesthetic consequences. Nonetheless, early recognition and treatment of malignant tumors of the eyelid margin is mandatory. Line-field confocal optical coherence tomography (LC-OCT) is a novel tool for the in vivo, real-time skin imaging. OBJECTIVES: The aim of the study was to identify and analyze the LC-OCT features of a series of eyelid margin growths and to correlate these features with the histopathological findings. METHODS: Patients with eyelid margin growths who were scheduled for lesion excision underwent LC-OCT examination. Inclusion criteria were a challenging clinical aspect of the lesions and a clinical history of recent onset (up to 12 months). In all cases, the histopathological examination of the excised lesions was performed for the final diagnosis. RESULTS: A total of 31 lesions located on the upper (13 cases) or lower (18 cases) eyelid margin from 28 consecutive patients (male = 15, female = 13; mean age: 64.7 years, range: 44-87 years) were evaluated and excised. The histopathologic diagnoses were nodular basal cell carcinoma (BCC) (nine cases), squamous cell carcinoma (SCC) (three cases), compound nevus (four cases), dermal nevus (two cases), seborrheic keratosis (four cases), pyogenic granuloma (one case), trichilemmal cyst (three cases), and hidrocystoma (five cases). LC-OCT allowed the in vivo recognition of the main microscopic features of the examined lesions. CONCLUSIONS: LC-OCT represents a promising tool for the evaluation of eyelid margin lesions. Advantages of non-invasive diagnosis particularly relevant in such a sensitive region include a more correct planning of the treatment and, in case of surgery, the most appropriate surgical approach and, importantly, a correct timing of intervention.

Clinician's Ability to Identify Non-Melanoma Skin Cancer on 3D-Total Body Photography Sectors that Were Initially Identified during In-Person Skin Examination with Dermoscopy.

INTRODUCTION: Non-melanoma skin cancer (NMSC) is a cause of significant morbidity and mortality in high-risk individuals. Total body photography (TBP) is currently used to monitor melanocytic lesions in patients with high risk for melanoma. The authors examined if three-dimensional (3D)-TBP could be useful for diagnosis of NMSC. METHODS: Patients (n = 129; 52 female, 77 male) with lesions suspicious for NMSC who had not yet had a biopsy underwent clinical examination followed by examination of each lesion with 3D-TBP Vectra®WB360 (Canfield Scientific, Parsippany, NJ, USA) and dermoscopy. RESULTS: The 129 patients had a total of 182 lesions. Histological examination was performed for 158 lesions; the diagnoses included basal cell carcinoma (BCC; n = 107), squamous cell carcinoma (SCC; n = 27), in-situ SCC (n = 15). Lesions were located in the head/neck region (n = 138), trunk (n = 21), and limbs (n = 23). Of the 182 lesions examined, 12 were not visible on 3D-TBP; reasons for not being visible included location under hair and on septal of nose. Two lesions appeared only as erythema in 3D-TBP but were clearly identifiable on conventional photographs. Sensitivity of 3D-TBP was lower than that of dermoscopy for BCC (73% vs. 79%, p = 0.327), higher for SCC (81% vs. 74%, p = 0.727), and lower for in-situ SCC (0% vs. 33%, p = 125). Specificity of 3D-TBP was lower than that of dermoscopy for BCC (77% vs. 82%, 0.581), lower for SCC (75% vs. 84%, p = 0.063), and higher for in-situ SCC (97% vs. 94%, p = 0.344). Diagnostic accuracy of 3D-TBP was lower than that of dermoscopy for BCC (75% vs. 80%), lower for SCC (76% vs. 82%), and lower for in-situ SCC (88% vs. 89%). Lesion location was not associated with diagnostic confidence in dermoscopy (p = 0.152) or 3D-TBP (p = 0.353). If only lesions with high confidence were included in the calculation, diagnostic accuracy increased for BCC (n = 27; sensitivity 85%, specificity 85%, diagnostic accuracy 85%), SCC (n = 10; sensitivity 90%, specificity 80%, diagnostic accuracy 83%), and for in-situ SCC (n = 2; sensitivity 0%, specificity 100%, diagnostic accuracy 95%). CONCLUSION: Diagnostic accuracy appears to be slightly lower for 3D-TBP in comparison to dermoscopy. However, there is no statistically significant difference in the sensitivity and specificity of 3D-TBP and dermoscopy for NMSC. Diagnostic accuracy increases, if only lesions with high confidence are included in the calculation. Further studies are necessary to determine if 3D-TBP can improve management of NMSC.

Concordance of in vivo reflectance confocal microscopy and horizontal-sectioning histology in skin tumours.

BACKGROUND: In vivo reflectance confocal microscopy (RCM) enables the study of architectural and cytological aspects in horizontal sections, which closely correlate with histologic features. However, traditional histopathological vertical sections cannot totally reproduce the image of the in vivo RCM horizontal section. OBJECTIVE: To evaluate the concordance between in vivo RCM and histopathologic transverse sections for melanocytic lesions, basal cell carcinoma and seborrheic keratoses. METHODS: Prospectively collected benign melanocytic and non-melanocytic tumours diagnosed by dermoscopy were evaluated for common RCM features and compared to histopathology in horizontal sections with haematoxylin and eosin staining. RESULTS: A total of 44 skin tumours including 19 melanocytic lesions (nine compound, five junctional and five intradermal nevi), 12 basal cell carcinomas and 13 seborrheic keratoses were collected in the Department of Dermatology of Hospital Clinic of Barcelona. The RCM features that had statistically significant agreement with the histopathological horizontal sections were the preserved and visible honeycomb pattern, well defined DEJ, small bright particles, dermal nests, tumour islands and dark silhouettes, clefting, collagen bundles, thickened collagen bundles and cytologic atypia. CONCLUSIONS: Histopathology evaluation of horizontal sections of skin tumours can be correlated with main RCM findings. The results of this study have improved the understanding and interpretation of RCM features in relation to skin tumours, thus reinforcing the utility of RCM as a diagnostic tool.

High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.

Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection-over-union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images.

Semantic segmentation of basal cell carcinoma (BCC) from full-field optical coherence tomography (FF-OCT) images of human skin has received considerable attention in medical imaging. However, it is challenging for dermatopathologists to annotate the training data due to OCT's lack of color specificity. Very often, they are uncertain about the correctness of the annotations they made. In practice, annotations fraught with uncertainty profoundly impact the effectiveness of model training and hence the performance of BCC segmentation. To address this issue, we propose an approach to model training with uncertain annotations. The proposed approach includes a data selection strategy to mitigate the uncertainty of training data, a class expansion to consider sebaceous gland and hair follicle as additional classes to enhance the performance of BCC segmentation, and a self-supervised pre-training procedure to improve the initial weights of the segmentation model parameters. Furthermore, we develop three post-processing techniques to reduce the impact of speckle noise and image discontinuities on BCC segmentation. The mean Dice score of BCC of our model reaches 0.503±0.003, which, to the best of our knowledge, is the best performance to date for semantic segmentation of BCC from FF-OCT images.

'Pitfalls for differentiating basal cell carcinoma from non-basal cell carcinoma on optical coherence tomography: A clinical series'.

Optical coherence tomography (OCT), a non-invasive diagnostic modality, may replace biopsy for diagnosing basal cell carcinoma (BCC) if a high-confidence BCC diagnosis can be established. In other cases, biopsy remains necessary to establish a histopathological diagnosis and treatment regimen. It is, therefore, essential that OCT assessors have a high specificity for differentiating BCC from non-BCC lesions. To establish high-confidence BCC diagnoses, specific morphological BCC characteristics on OCT are used. This study aimed to review several cases of non-BCC lesions that were misclassified as BCC by experienced OCT assessors, thereby providing insight into the causes of these misclassifications and how they may be prevented. The study population consisted of patients who had a histopathologically-verified non-BCC lesion. Patients from Maastricht University Medical Center+ from February 2021 to April 2021 were included in the study. Two independent OCT assessors assessed OCT scans. One OCT assessor recorded the presence or absence of validated morphological BCC characteristics. A false-positive OCT test result was defined as certainty of BCC presence in a non-BCC lesion. The frequency of misclassifications and the presence or absence of morphological BCC features are discussed. A total of 124 patients with non-BCC lesions were included. Six patients were misclassified by both OCT assessors and are discussed in more detail. Histopathological diagnoses were squamous cell carcinoma (n = 2/21), actinic keratosis (n = 2/29), squamous cell carcinoma in situ/Bowen's disease (n = 1/16), or interphase dermatitis (n = 1/4). In all misclassified cases, multiple, apparent morphological BCC characteristics on OCT were present. Most non-BCC lesions are recognized as such by OCT assessors. However, there remains a small risk that a high-confidence BCC diagnosis is established in non-BCC lesions wherein features mimicking validated BCC characteristics are present. Misclassification may be prevented by careful delineation of epidermal layers and good differentiation between dermal ovoid structures typical of BCC versus squamous cell carcinoma.

Line-Field Confocal Optical Coherence Tomography for the Diagnosis of Skin Carcinomas: Real-Life Data over Three Years.

Line-field confocal optical coherence tomography (LC-OCT) can help the clinical diagnosis of skin diseases. The present study aimed to evaluate the sensitivity, specificity, and diagnostic accuracy of LC-OCT for the diagnosis of the most frequent non-melanoma skin cancers (NMSCs), i.e., basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Comparing LC-OCT diagnostic performances with those of dermoscopy, histopathological examination was used as a gold standard. For every study endpoint, the diagnostic ability of LC-OCT revealed superiority over the dermoscopic examination. In particular, a significant increase in specificity was observed. Sensitivity, specificity, and diagnostic accuracy of dermoscopy and LC-OCT for the diagnosis of malignancy were, respectively, 0.97 (CI 0.94-0.99), 0.43 (CI 0.36-0.51), and 0.77 (CI 0.72-0.81) for dermoscopy and 0.99 (CI 0.97-1.00), 0.90 (CI 0.84-0.94), and 0.96 (CI 0.93-0.97) for LC-OCT. The positive predictive value (PPV) resulted in 0.74 (CI 0.69-0.78) for dermoscopy and 0.94 (CI 0.91-0.97) for LC-OCT, and the negative predictive value (NPV) was 0.89 (CI 0.81-0.95) for dermoscopy and 0.98 (CI 0.95-1.00) for LC-OCT. Finally, our real-life study showed a potentially important role of LC-OCT in the non-invasive diagnosis of NMSCs, especially BCC. The real-time imaging technique could spare unnecessary biopsies with an increased sensitivity, a much higher specificity, and better accuracy than clinical assessment with dermoscopy alone.

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning.

Significance: An integrated cellular-resolution optical coherence tomography (OCT) module with near-infrared Raman spectroscopy was developed on the discrimination of various skin cancer cells and normal cells. Micron-level three-dimensional (3D) spatial resolution and the spectroscopic capability on chemical component determination can be obtained simultaneously. Aim: We experimentally verified the effectiveness of morphology, intensity, and spectroscopy features for discriminating skin cells. Approach: Both spatial and spectroscopic features were employed for the discrimination of five types of skin cells, including keratinocytes (HaCaT), the cell line of squamous cell carcinoma (A431), the cell line of basal cell carcinoma (BCC-1/KMC), primary melanocytes, and the cell line of melanoma (A375). The cell volume, compactness, surface roughness, average intensity, and internal intensity standard deviation were extracted from the 3D OCT images. After removing the fluorescence components from the acquired Raman spectra, the entire spectra (600 to 2100 cm-1) were used. Results: An accuracy of 85% in classifying five types of skin cells was achieved. The cellular-resolution OCT images effectively differentiate cancer and normal cells, whereas Raman spectroscopy can distinguish the cancer cells with nearly 100% accuracy. Conclusions: Among the OCT image features, cell surface roughness, internal average intensity, and standard deviation of internal intensity distribution effectively differentiate the cancerous and normal cells. The three features also worked well in sorting the keratinocyte and melanocyte. Using the full Raman spectra, the melanoma and keratinocyte-based cell carcinoma cancer cells can be discriminated effectively.