Variance of Basal Cell Carcinoma Subtype Reporting by Practice Setting

Moon, Deborah J.; Higgins, Shauna; Feinstein, Shera; Ahadiat, Omeed; Sutton, Adam; Wysong, Ashley

doi:10.1001/jamadermatol.2019.0066

Cited by 4 publications

(8 citation statements)

References 6 publications

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“…The correct assessment of the subtype is crucial for planning the relevant treatment. However, there is a significant inter-pathologist variability when grading tumors 8 and reporting the subtype 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

Yacob

Siarov

Villiamsson

et al. 2023

Sci Rep

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The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.

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Section: Introductionmentioning

confidence: 99%

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

Yacob

Siarov

Villiamsson

et al. 2023

Sci Rep

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“…Tumor location divided by Mohs appropriate use criteria regions. 8 “H” refers to mask areas of central face, “M” refers to cheeks, forehead, scalp, neck, and jawline, and “L” refers to trunk and extremities. Area H (orange) had 106 aggressive subtypes found on Mohs of the 291 nonaggressive biopsies from the region.…”

Section: Resultsmentioning

confidence: 99%

“…Of note, our group has previously observed that 10% of referred patients had no specification of BCC subtype, almost half of whom had aggressive histology on Mohs. 8 Although BCCs lacking subtypes on biopsy reports were excluded from our analyses, it is worth noting that in practice, differences in reporting patterns may contribute to an even higher misrepresentation on biopsy of a tumor's true aggressiveness.…”

Section: Discussionmentioning

confidence: 99%

Misclassification of Aggressive Basal Cell Carcinoma Subtypes and Implications for Management

et al. 2021

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BACKGROUND Management of basal cell carcinoma (BCC) varies by histopathologic subtype; however, biopsies may inadequately characterize them as nonaggressive, risking potential suboptimal treatment. OBJECTIVE To characterize the rate of undetected aggressive BCC subtypes by size, location, and histopathology type. MATERIALS AND METHODS Retrospective cohort study of 928 BCCs treated with Mohs Micrographic Surgery (MMS) at a tertiary academic institution from 2015 to 2017, comparing patient and tumor characteristics and histopathologic subtype on biopsy versus Mohs. RESULTS Among the 825 BCCs with known subtypes on biopsy, 68% (561/825) were classified as nonaggressive, 28% (159/561) of which were subsequently found to have aggressive subtypes on MMS. Aggressive features were more often underrepresented in biopsy samples taken from Area H compared with Area M/L (odd ratio [OR] 2.65, 95% confidence interval [CI] 1.73-4.08, p , .001) or those with nodular subtypes (OR 2.19, CI 1.08-4.45, p 5 .03). Of concern, these unsuspected aggressive BCCs required more Mohs stages for clearance (mean 2.37, SD 0.72, p , .001) compared with BCCs that remained nonaggressive on both biopsy and Mohs (mean 1.50, SD 0.75). CONCLUSION Given the high percentage of BCCs with unsuspected aggressive subtypes, higher clinical suspicion for undiagnosed high-risk BCCs should be given to nodular BCCs and to BCCs on Area H.

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“…After an initial qualitative review by the steering group, consultation of the current literature and exclusion of subtypes not suggested by at least four experts, the included subtypes comprised nodular BCC, superficial BCC and less common variants (encompassing infiltrative and morpheaform BCCs). [27][28][29] The number of proposed image features for each specific BCC subtype varied from 0 to 40. The terminology for seven image features (25 words in total) was synonyms.…”

Section: First Delphi Roundmentioning

confidence: 99%

2021 international consensus statement on optical coherence tomography for basal cell carcinoma: image characteristics, terminology and educational needs

Fuchs

Ortner

Mogensen

et al. 2022

Acad Dermatol Venereol

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Background Despite the widespread use of optical coherence tomography (OCT) for imaging of keratinocyte carcinoma, we lack an expert consensus on the characteristic OCT features of basal cell carcinoma (BCC), an internationally vetted set of OCT terms to describe various BCC subtypes, and an educational needs assessment. Objectives To identify relevant BCC features in OCT images, propose terminology based on inputs from an expert panel and identify content for a BCC‐specific curriculum for OCT trainees. Methods Over three rounds, we conducted a Delphi consensus study on BCC features and terminology between March and September 2020. In the first round, experts were asked to propose BCC subtypes discriminable by OCT, provide OCT image features for each proposed BCC subtypes and suggest content for a BCC‐specific OCT training curriculum. If agreement on a BCC‐OCT feature exceeded 67%, the feature was accepted and included in a final review. In the second round, experts had to re‐evaluate features with less than 67% agreement and rank the ten most relevant BCC OCT image features for superficial BCC, nodular BCC and infiltrative and morpheaphorm BCC subtypes. In the final round, experts received the OCT‐BCC consensus list for a final review, comments and confirmation. Results The Delphi included six key opinion leaders and 22 experts. Consensus was found on terminology for three OCT BCC image features: (i) hyporeflective areas, (ii) hyperreflective areas and (iii) ovoid structures. Further, the participants ranked the ten most relevant image features for nodular, superficial, infiltrative and morpheaform BCC. The target group and the key components for a curriculum for OCT imaging of BCC have been defined. Conclusion We have established a set of OCT image features for BCC and preferred terminology. A comprehensive curriculum based on the expert suggestions will help implement OCT imaging of BCC in clinical and research settings.

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Variance of Basal Cell Carcinoma Subtype Reporting by Practice Setting

Abstract: Ms Morrison had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Cited by 4 publications

References 6 publications

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

Misclassification of Aggressive Basal Cell Carcinoma Subtypes and Implications for Management

2021 international consensus statement on optical coherence tomography for basal cell carcinoma: image characteristics, terminology and educational needs

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