Challenges of Frozen Section in Thoracic Pathology: Lepidic Lesions, Limited Resections, and Margins

Borczuk, Alain C.

doi:10.5858/arpa.2016-0415-ra

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…We also found 44 (21.4%) equivocal or inadequate results with the SIC diagnosis. Thus, we recommend that FSH should be used in addition to SIC for the best qualitative intraoperative diagnosis, except in cases deemed a potential biohazard risk for the pathology staff following SIC, as documented by Sugiyama et al . Nevertheless, intraoperative SIC shows fewer nuclear artifacts than FSH samples, enabling diagnoses of carcinoids or small cell carcinomas to be declared as the final pathological diagnosis during surgery.…”

Section: Discussionmentioning

confidence: 92%

“…In particular, as shown in Fig. a,b, it is not easy to distinguish invasive mucinous carcinoma from goblet cell metaplasia/hyperplasia by SIC because of the monotonous columnar cells producing a mucinous background in the absence of necrosis, and these characteristics might be interpreted as those of a benign lesion, especially under the time constraints of intraoperative assessment. Also, marked tumor cell necrosis or changes in viable tumor cells due to preoperative chemotherapy could have influenced the results of SIC .…”

Section: Discussionmentioning

confidence: 99%

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The approach of scratch‐imprint cytology: Is it an alternative to frozen section for intraoperative assessment of pulmonary lesions?

Sugiyama

Tajiri

Fujita

et al. 2019

Pathology International

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To address the diagnostic performance of scratch-imprint cytology (SIC), in this study we compared intraoperative diagnoses of pulmonary lesions between SIC and frozen section histology (FSH) for accuracy with respect to the final pathological diagnosis. We histologically divided 206 pulmonary lesions (resected surgically) into two groups (benign and malignant) and compared each intraoperative diagnosis by SIC and FSH with the final pathological diagnoses. We also examined the radiological existence of pure ground-glass opacity (GGO) nodules in each group. The diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 91.5%, 100%, 100%, 63.6%, and 92.6%, respectively for SIC, and 98.2%, 100%, 100%, 92.1% and 98.5%, respectively, for FSH. Thus, we concluded that diagnosis by SIC is reliable for malignancy, but not for benign lesions. All pure GGO nodules (19; 9.2%) were noninfectious and malignant with a high accuracy of FSH diagnosis (100%), in comparison with those of low accuracy with a SIC diagnosis (57.9%). SIC can be an appropriate intraoperative diagnostic tool where multiple cytotechnologists observe intraoperative SIC preparations scratched evenly across the whole lesion including the peripheral area of the mass. K E Y W O R D S equivocal diagnosis, frozen section diagnosis, intraoperative diagnosis, qualitative diagnosis, scratch-imprint cytology

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

The approach of scratch‐imprint cytology: Is it an alternative to frozen section for intraoperative assessment of pulmonary lesions?

Sugiyama

Tajiri

Fujita

et al. 2019

Pathology International

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“…Although the FS can be a precise diagnostic method to guide intraoperative resection procedures for lung adenocarcinoma, it remains difficult to recommend a definitive assessment by FS alone [34]. Borczuk suggested that combining clinical and radiologic information with FS could reduce diagnostic errors [35]. Our results show no significant difference in the AUC values between the FSRV and FSV models, but the former model has better accuracy and calibration.…”

Section: Discussionmentioning

confidence: 54%

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Woodruff

Sanduleanu

et al. 2020

Eur Radiol

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Objectives Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). Methods This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. ResultsThe radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. Conclusions Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. Key Points • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

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“…Considering the performance of radiomics model, we further selected clinical characteristics and frozen section that were strong predictors to build nomogram for predicting LNM.Intraoperative FS was advocated as a useful method for surgical planning, as it could provide more precise diagnosis. Previous studies illustrated that FS combined clinical and radiological features showed good performance and diagnostic accuracy[38][39][40]. Wu et al[39] investigated a CT-based radiomics model and combined it with FS and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM).…”

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confidence: 99%

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section: a retrospective study

Lü

Hou

et al. 2023

Preprint

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Purpose To evaluate the diagnostic efficiency among clinical model, radiomics model and nomogram that combined radiomics features and frozen section (FS) analysis for the prediction of lymph node (LN) metastasis for the patients with papillary thyroid cancer (PTC). Methods A total of 208 patients with PTC were retrospectively enrolled. The patients were divided into two groups randomly for training groups and validation groups. The Least absolute shrinkage and selection operator (LASSO) regression were used for the selection of radiomics feature extracted from ultrasound (US) images. Univariate and multivariate logistic analysis were used to select predictors including clinical characteristics and FS associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established. Results Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors to predict lymph node metastasis (LNM). The results showed that in the training group, nomogram had better performance than clinical model (P > 0.05) and radiomics model (P < 0.05). In the validation group, the results were similar to the training group, nomogram had a little higher diagnostic efficiency than clinical model (P > 0.05) and radiomic model (P > 0.05). Both in the training and validation group, nomogram had minor non-significant improvements in AUC compared to clinical model and significant improvements compared to radiomic model, however, the sensitivity of nomogram was a little higher. Conclusion We proposed that the nomogram combined the radiomics features and FS had the promise to create a substantial biomarker for predicting LNM of patients with PTC.

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Challenges of Frozen Section in Thoracic Pathology: Lepidic Lesions, Limited Resections, and Margins

Abstract: - Frozen section diagnosis is overall accurate in assessment of lepidic lesions. Pitfalls include rare benign mimickers and more common reactive lesions. Standard approaches to gross assessment and margin measurement require further research with increasing use of lung-sparing resections.

Cited by 10 publications

References 20 publications

The approach of scratch‐imprint cytology: Is it an alternative to frozen section for intraoperative assessment of pulmonary lesions?

The approach of scratch‐imprint cytology: Is it an alternative to frozen section for intraoperative assessment of pulmonary lesions?

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section: a retrospective study

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