Nuclear score evaluation in follicular-patterned thyroid lesions using optical and digital environments

Optimizing the management of thyroid specimens to efficiently generate whole slide images for diagnosis

Transition from optical to digital observation requires an additional procedure in the pathology laboratory, the scanning of glass slides, leading to increased time and digital archive consumption. Thyroid surgical samples often carry the need to collect several tissue fragments that generate many slides to be scanned. This study evaluated the impact of using different inking colours for the surgical margin, section thickness, and glass slide type, in the consumption of time and archive. The series comprehended 40 nodules from 30 patients, including 34 benign nodules in follicular nodular disease, 1 NIFTP, and 5 papillary carcinomas. In 12 nodules, the dominant pattern was microfollicular/solid and in 28 it was macrofollicular. Scanning times/mm2 were longer in red-inked fragments in comparison to green (p = 0.04) and black ones (p = 0.024), and in blue-inked in comparison to green ones (p = 0.043). File sizes/mm2 were larger in red-inked fragments in comparison to green (p = 0.008) and black ones (p = 0.002). The dominant pattern microfollicular/solid was associated with bigger file size/mm2 in comparison with the macrofollicular one (p < 0.001). All scanner outputs increase significantly with the thickness of the section. All scanning outputs increase with the usage of adhesive glass slides in comparison to non-adhesive ones. Small interventions in thyroid sample management that can help optimizing the digital workflow include to prefer black and green inking colours for the surgical margins and 2 µm section in non-adhesive glass slides for increased efficiency.

How many more slides to go until we fully adopt digital cytology?

Two‐liner/synopsis: The digital cytology hub (DCH) has been established under the umbrella of the Cytopathology journal. DCH will help bring about the crucial changes needed to make digital cytology the way of practicing cytology in our laboratories.

Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model

BACKGROUND

Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration.

METHODS

We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio.

RESULTS

This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value.

CONCLUSIONS

The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.

Digital analyses of nuclear features can help discriminate among non-invasive follicular thyroid neoplasm with papillary-like nuclear features, papillary thyroid carcinoma follicular subtype, and follicular carcinoma in cytological specimens

BACKGROUND

As it stands, the diagnosis of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is primarily based on histological analysis. We hypothesised that computerised analysis of nuclear images of cytological specimens could be used to differentiate NIFTP from papillary thyroid carcinoma follicular subtype (PTCFS) and follicular carcinoma (FC), influencing patient management.

METHODS

We employed a retrospective analytical observational study based on nuclear morphometric variables of cytological material from thyroid nodules classified as PTCFS, NIFTP, or FC. Five cases of each entity were analysed. Cytological slides were photographed, and 1170 cells for each entity were analysed digitally. The captured images were evaluated (blindly) using the ImageJ software package. The morphometric evaluation included area, perimeter, width, height, and circularity. Numerical variables were expressed as mean, median, minimum, and maximum (min; max) values. Kruskal-Wallis and Dunn's tests were used with a 5% significance level.

RESULTS

Regarding nuclear analysis, all variables differed among the three groups (p < 0.001). Given the interdependence among the variables, these data indicated that nuclear size was greatest in the NIFTP group, followed by FC and PTCFS.

DISCUSSION/CONCLUSION

Our analysis of the digital images, with a focus on nuclear parameters, found significantly difference among cytological specimens from cases of NIFTP, PTCFS and FC. Thus, this tool has the potential to provide additional information that may help in the diagnosis of NIFTP, even during the preoperative period. Additional studies are needed to create protocols, evaluate the applicability of nuclear morphological and morphometric parameters-focusing on digital pathology-and create algorithms and tools to assist cytopathologists with their diagnostic routines.

Successful integration of thyroid cytopathology and surgical pathology education in an E-module format

Context

The shift to digital learning in medicine is well underway and in fact spurred by the COVID-19 pandemic. The didactic portion of our institution's cytotechnology (CT) education program is online and delivered to learners across the nation. With CT education elevating to the master’s degree level, there is a need to expand cytologic correlation with surgical resection specimens. We also wanted to afford pathology residents the same.

Methods

We developed an online cytologic–histologic correlation digital learning module (e-module) addressing thyroid fine needle aspirations (FNAs) and surgical thyroidectomy specimens which was administered as part of coursework in the CT education and pathology residency programs. The module was 35 min long and consisted of guided narration with both formative and summative interactive quizzes. After completion of the module, participants were invited to fill a brief survey comprised of multiple choice, Likert, and free response questions. This study was approved by the institutional review board.

Results

The 29 respondents were comprised of 22 CT students and 7 residents. CT students had minimal experience thyroid pathology prior to the module; residents were mixed. Twenty-three (79.3%) ranked the highest tiers for learning cytopathology through this module, 24 (82.8%) for learning thyroid surgical pathology, and 25 (86.2%) for cytologic–histologic correlation. All respondents stated they would like similar activities in the future.

Conclusions

Teaching cytology–histology correlation for thyroid in an electronic format was effective and well-received by participants. There is a demand for these activities among current learners, suggesting that expanding the available repertoire will be beneficial.