Digital Versus Optical Diagnosis of Follicular Patterned Thyroid Lesions

Role of artificial intelligence in digital pathology for gynecological cancers

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

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.

Morphometric Analysis of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Digital Pathology

Digital pathology uses digitized images for cancer research. We aimed to assess morphometric parameters using digital pathology for predicting recurrence in patients with papillary thyroid carcinoma (PTC) and lateral cervical lymph node (LN) metastasis. We analyzed 316 PTC patients and assessed the longest diameter and largest area of metastatic focus in LNs using a whole slide imaging scanner. In digital pathology assessment, the longest diameters and largest areas of metastatic foci in LNs were positively correlated with traditional optically measured diameters (R = 0.928 and R2 = 0.727, p < 0.001 and p < 0.001, respectively). The optimal cutoff diameter was 8.0 mm in both traditional microscopic (p = 0.009) and digital pathology (p = 0.016) evaluations, with significant differences in progression-free survival (PFS) observed at this cutoff (p = 0.006 and p = 0.002, respectively). The predictive area's cutoff was 35.6 mm2 (p = 0.005), which significantly affected PFS (p = 0.015). Using an 8.0-mm cutoff in traditional microscopic evaluation and a 35.6-mm2 cutoff in digital pathology showed comparable predictive results using the proportion of variation explained (PVE) methods (2.6% vs. 2.4%). Excluding cases with predominant cystic changes in LNs, the largest metastatic areas by digital pathology had the highest PVE at 3.9%. Furthermore, high volume of LN metastasis (p = 0.001), extranodal extension (p = 0.047), and high ratio of metastatic LNs (p = 0.006) were associated with poor prognosis. Both traditional microscopic and digital pathology evaluations effectively measured the longest diameter of metastatic foci in LNs. Moreover, digital pathology offers limited advantages in predicting PFS of patients with lateral cervical LN metastasis of PTC, especially those without predominant cystic changes in LNs.

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 Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

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

INTRODUCTION

The subjective evaluation of nuclear features in follicular-patterned lesions of the thyroid is a reason for diagnosis discordance. The assessment of nuclear features also varies whether the observation is performed optically or digitally. Our objective was to study the concordance among pathologists regarding the nuclear score (NS) evaluation in a series of follicular-patterned lesions, using optical versus three digital scanning protocols.

METHODS

Three pathologists evaluated the NS in a 3mm² area randomly selected from 20 hematoxylin-eosin slides representative of the respective 20 follicular-patterned thyroid lesions. The NS evaluation was performed using optical and three different scanning protocols in two scanners: P1000_20x, P1000_40x and DP200_20x. Kappa statistic (κ) and intraclass correlation coefficient (ICC) were obtained for intra- and interpathologist concordance.

RESULTS

We recorded a good agreement among pathologists in the optical evaluation of the NS (ICC of 0.73). The concordance between optical versus digital observation had an almost perfect agreement for P1000_20x [κ = 0.85 (0.67-1.02); p < 0.0001] and a substantial agreement for both P1000_40x [κ = 0.69 (0.43-0.95) p = 0.002] and DP200_20x [κ = 0.77 (0.57-0.97); p = 0.001]. The P1000_20x protocol had the best intrapathologist concordance with the optical method, classified as almost perfect agreement for pathologists A (80%) and B (85%), and substantial agreement for pathologist C (70%).

CONCLUSION

Digital observation of the WSI is valid for the NS evaluation in follicular-patterned thyroid lesions, with good agreement among pathologists and between optical and scanning protocols. Performance studies and validation procedures cannot be avoided in this setting to prevent diagnostic discordance due to the scanning process.

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma

Whole-slide imaging (WSI) has wide spectrum of application in histopathology, especially in the study of cancer including papillary thyroid carcinoma. The main applications of WSI system include research, teaching, and assessment and recently pathology practices. The other major advantages of WSI over histological sections on glass slides are easier storage and sharing of information as well as adaptation of use in artificial intelligence. The applications of WSI depend on factors such as volume of services requiring WSI, physical factors (computer server, bandwidth limitation of networks, storages requirements for data), adaption of the WSI images with the laboratory workflow, personnel (IT expert, pathologist, technicians) adaptation to the WSI workflow, validation studies, ethics, and cost efficiency of the application(s).

Rethinking Low-Risk Papillary Thyroid Cancers < 1cm (Papillary Microcarcinomas): An Evidence Review for Recalibrating Diagnostic Thresholds and/or Alternative Labels

Background: Recalibrating diagnostic thresholds or using alternative labels may mitigate overdiagnosis and overtreatment of papillary microcarcinoma (mPTC). We aimed at identifying and collating relevant epidemiological evidence on mPTC, to assess the case for recalibration and/or new labels. Methods: We searched EMBASE and PubMed databases from inception to December 2020 for natural history, autopsy, diagnostic drift, and diagnostic reproducibility studies. Where a relevant systematic review was pre-identified, only new articles were additionally included. Non-English articles were excluded. One author screened titles and abstracts. Two authors screened full text articles, performed quality assessments, and extracted data. We undertook narrative synthesis of included evidence (pooled estimates from systematic reviews and single estimates from primary studies). Results: One systematic review of patients undergoing active surveillance found that after 5 years of follow-up, 5.3% (95% confidence interval [CI 4.4-6.4%]) of the mPTC lesions had increased in size by ≥3 mm, and 1.6% [CI 1.1-2.4%] of patients had lymph node metastases. Among 7 new primary studies (including 3 updates on 2 studies included in the systematic review), 1-5% of patients undergoing active surveillance had lymph node metastases after a median follow-up of 1-10 years. One systematic review found that subclinical thyroid cancer incidentally discovered at autopsy is relatively common, with a pooled prevalence of 11.2% [CI 6.7-16.1%] among studies that examined the whole thyroid. Four diagnostic drift studies evaluated the new classification of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Three studies of cases previously diagnosed as papillary thyroid cancer found 1.3-2.3% were reclassified as NIFTP (reclassifications were from follicular variation of papillary thyroid cancer [FVPTC]). One study of 48 cases previously diagnosed as mPTC found that 23.5% were reclassified as NIFTP. Thirteen reproducibility studies of papillary thyroid lesions found substantial variation in the histopathological diagnosis of thyroid lesions, including FVPTC and NIFTP classifications (no study evaluated mPTC). Conclusions: This review supports consideration of recalibrating diagnostic thresholds and/or alternative labels for low-risk mPTC.