Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm

Sendín‐Martin, Mercedes; Lara-Caro, Manuel; Harris, Ucalene; Moronta, Matthew; Rossi, Anthony; Lee, Erica; Chen, Chih‐Shan Jason; Nehal, Kishwer S.; Sánchez, Julián Conejo-Mir; Pereyra‐Rodriguez, José Juan; Jain, Manu

doi:10.1016/j.jid.2021.09.029

Cited by 17 publications

(8 citation statements)

References 37 publications

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“…EVCM may facilitate BCC diagnosis and treatment in one visit (“one-stop shop”) [24] and potentially replace the traditional histopathological examination of paraffin-embedded punch biopsies. In the near future, the detection of BCC in EVCM images could be semiautomatically done with the help of a deep learning algorithm [25]. Further larger clinical trials are required to compare EVCM imaging versus standard care for surgical treatment in the patient with clinically suspected BCC.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Digital Staining ex vivo Confocal Microscopy for Diagnosing and Subtyping Basal Cell Carcinoma in Fresh Pretherapeutic Punch Biopsies: A Monocentric Prospective Study

et al. 2022

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Background: Ex vivo confocal microscopy using fusion mode and digital staining (EVCM) scans unfixed fresh tissue and produces rapidly digitally stained images of very similar quality to classical pathology. We investigated whether EVCM could represent an alternative to the standard histological examination of the pretherapeutic basal cell carcinoma (BCC) punch biopsies. Objectives: The objective of the study was to assess diagnostic accuracy of EVCM versus traditional histopathological examination for diagnosing and subtyping clinically suspicious lesions of BCC in 3-mm fresh and nonfixed punch biopsies. Methods: In this prospective monocentric observational study, patients with clinically suspected BCC were consecutively enrolled. Punch biopsies were imaged using EVCM and subsequently processed for standard histologic examination (gold standard). EVCM images were examined by a dermatopathologist blinded to clinical aspect of the lesion and histopathological results. Concordance between the EVCM and histology analysis was calculated with Cohen’s kappa (κ) statistic. Results: Sixty-six patients were recruited, and 106 biopsies were analyzed. EVCM correctly diagnosed 70/73 BCCs and 31/33 non-BCC lesions, corresponding to a sensitivity of 96% and a specificity of 94% (positive predictive value = 97%, negative predictive value = 91%). The EVCM assessment led to over-staging and under-staging of BCC subtypes in 5% and 11% of cases, respectively. It led to over-staging and under-staging of BCC depths in 5% and 15%, respectively. The kappa coefficient for concordance was 0.78 (95% confidence interval [CI]: 0.69–0.88) when considering BCC subtypes and 0.81 (95% CI: 0.72–0.90) when considering BCC depths. Conclusions: These results render EVCM as a promising option for “real-time” pretreatment evaluation of clinically suspected BCC lesions. Further larger randomized studies are needed to assess the efficiency of EVCM versus standard care in patients with clinically suspected BCC.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Digital Staining ex vivo Confocal Microscopy for Diagnosing and Subtyping Basal Cell Carcinoma in Fresh Pretherapeutic Punch Biopsies: A Monocentric Prospective Study

et al. 2022

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“…This would improve visualization of the OCT images and reduce the learning curve. Moreover, AI can aid in the automated detection of tumors, leading to its wider adoption [20,30]. This work is a preliminary trial to see the feasibility of cellular-resolution OCT on segmentation/classification of ex-vivo human cancerous tissues.…”

Section: Discussionmentioning

confidence: 99%

“…CNN has the advantage of automatically extracting features and is not limited to features defined by the human eye. At present, many studies have used CNN to identify basal cell carcinoma in stained images [20] and segment nuclei from stained images [21,22] and dermal fillers in OCT images of mouse skin [23].…”

Section: Introductionmentioning

confidence: 99%

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

Jain,

Chang,

Singh

et al. 2023

Journal of Biophotonics

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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.

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“…Although MPM device can improve the diagnosis of BCC in vivo and reduce unnecessary biopsies, reading these images remains challenging for a novice. 32 To expand the widespread adaptation of this technique, artificial intelligence algorithms can be built for automated detection of BCC, similar to the work done for the detection of BCC on RCM 35,36 and on hepatocellular carcinoma detection using MPM. 37,38 In conclusion, we present the results of an ex vivo collagen…”

Section: Ta B L E 2 (Continued)mentioning

confidence: 99%

Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy

Sendín‐Martin

Posner

Harris

et al. 2022

Experimental Dermatology

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Basal cell carcinoma (BCC) is the most common skin cancer, and its incidence is rising. Millions of benign biopsies are performed annually for BCC diagnosis, increasing morbidity, and healthcare costs. Non‐invasive in vivo technologies such as multiphoton microscopy (MPM) can aid in diagnosing BCC, reducing the need for biopsies. Furthermore, the second harmonic generation (SHG) signal generated from MPM can classify and prognosticate cancers based on extracellular matrix changes, especially collagen type I. We explored the potential of MPM to differentiate collagen changes associated with different BCC subtypes compared to normal skin structures and benign lesions. Quantitative analysis such as frequency band energy analysis in Fourier domain, CurveAlign and CT‐FIRE fibre analysis was performed on SHG images from 52 BCC and 12 benign lesions samples. Our results showed that collagen distribution is more aligned surrounding BCCs nests compared to the skin's normal structures (p < 0.001) and benign lesions (p < 0.001). Also, collagen was orientated more parallelly surrounding indolent BCC subtypes (superficial and nodular) versus those with more aggressive behaviour (infiltrative BCC) (p = 0.021). In conclusion, SHG signal from type I collagen can aid not only in the diagnosis of BCC but could be useful for prognosticating these tumors. Our initial results are limited to a small number of samples, requiring large‐scale studies to validate them. These findings represent the groundwork for future in vivo MPM for diagnosis and prognosis of BCC.

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Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm

Cited by 17 publications

References 37 publications

Diagnostic Accuracy of Digital Staining ex vivo Confocal Microscopy for Diagnosing and Subtyping Basal Cell Carcinoma in Fresh Pretherapeutic Punch Biopsies: A Monocentric Prospective Study

Diagnostic Accuracy of Digital Staining ex vivo Confocal Microscopy for Diagnosing and Subtyping Basal Cell Carcinoma in Fresh Pretherapeutic Punch Biopsies: A Monocentric Prospective Study

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

Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy

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