Applications of machine and deep learning to thyroid cytology and histopathology: a review.

This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images

Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images

Background: Thyroid nodules diagnosed as 'atypia of undetermined significance/ follicular lesion of undetermined significance' (AUS/FLUS) or 'follicular neoplasm/ suspected follicular neoplasm' (FN/SFN), according to Bethesda’s classification, represena challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy. Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n = 68) or FN/SFN (n = 33) from 97 thyroidectomy patients. Slides with cytological material were submitted for manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the classification and regression trees gini model. The intraclass coefficient of correlation was used to evaluate method reproducibility. Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for entropy (P < 0.05), while the FN/SFN nodules differed for fractal analysis, coefficient of variation (CV) of roughness, and CV-entropy (P < 0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0 and 100.0% malignant nodules, with a correct global classification of 94.1 and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61–0.93) in 10 of the 12 nuclear parameters evaluated. Conclusion: CANI demonstrated a high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology.This study received financial support from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; processes number 2016/14987-0 and number 2016/14988-6). Further funding through 'Fundação para a Ciência e Tecnologi' – FCT and FEDER 'Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020'; by Operacional Programme for Competitiveness and Internationalization 'POCI' (Grant no. POCI-01-0145-FEDER-007274); by the 'Advancing cancer research: from basic knowledge to application' (grant no. NORTE-01-0145-FEDER-000029); and by the 'Projetos Estruturados de I & D & I', funded by Norte 2020 – Programa Operacional Regional do Norte

An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Deep Features

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challengeable for the under-promoted upstream encoders, while the unused spatial representations of cervical cells are the available features to supply the semantics analysis. As well as patches sampling with overlap and repetitive processing incur the inefficiency and the unpredictable side effect. This study designs a novel inline connection network (InCNet) by enriching the multi-scale connectivity to build the lightweight model named You Only Look Cytopathology Once (YOLCO) with the additional supervision of spatial information. The proposed model allows the input size enlarged to megapixel that can stitch the WSI without any overlap by the average repeats decreased from $10^3\sim10^4$ to $10^1\sim10^2$ for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task features, the experimental results appear $0.872$ AUC score better and $2.51\times$ faster than the best conventional method in WSI classification on multicohort datasets of 2,019 slides from four scanning devices.Comment: 16 pages, 8 figures, already submitted to Medical Image Analysi

Utilizzo di scores multiparametrici nella caratterizzazione del rischio stimato di malignità di noduli tiroidei sottoposti a citologia per ago sottile

Scopo Le società scientifiche hanno adottato sistemi per la classificazione ecografica dei noduli tiroidei, con l’obiettivo di ridurre gli agoaspirati senza perdere neoplasie clinicamente rilevanti. L’obiettivo del progetto è stato la validazione prospettica dell’accuratezza diagnostica di tali sistemi e la loro potenziale integrazione con i dati citologici tradizionali e di biologia molecolare. Metodi Sono stati prospetticamente valutati noduli sottoposti ad agoaspirato ecoguidato. Le caratteristiche ultrasonografiche sono state registrate ed utilizzate per classificare ciascun nodulo secondo le linee guida American Association of Clinical Endocrinologists (AACE/ACE/AME), American College of Radiologists (ACR), American Thyroid Association (ATA), EU-TIRADS e K-TIRADS. Lo standard di riferimento è l’istologia definitiva se disponibile, oppure una citologia benigna con successivo follow-up. Sono stati escluse citologie non diagnostiche o indeterminate. E’stato raccolto materiale residuo in soluzione conservante gli acidi nucleici, per studi di Next Generation Sequencing su pannello custom per carcinoma tiroideo. Risultati Sono stati campionati 917 noduli, di cui 82 sono stati esclusi per dimensioni <1 cm e 282 per assenza di diagnosi conclusiva. L’applicazione dei sistemi di classificazione permetterebbe di evitare da 92 (16.6%) a 287 (51.9%) agoaspirati (sistema K-TIRADS e ACR TIRADS, rispettivamente [p<0.001], con un false-negative rate di 3.3% e 2.8%). Il tasso di malignità nelle varie categorie risulta congruente con il rischio stimato. Conclusioni La stratificazione ecografica permette una migliore selezione dei noduli candidati a citologia ed eventuale analisi molecolare, attraverso la stima del rischio di malignità pre-test, ottimizzando i valori predittivi risultanti. I vari sistemi presentano differenze significative nel numero di prelievi evitabili

Raman Microspectroscopy for the Discrimination of Thyroid and Lung Cancer Subtypes for Application in Clinical Cytopathology

The branch of cytology known as cytopathology, studies and diagnoses diseases at a cellular level, and is a useful method for detecting cancer. The procedures used to attain cytological samples for diagnostic purposes, such as aspiration and exfoliative methods are safe, accurate and cost-effective. Histochemical and immunohistochemical (IHC) techniques are commonly applied to cytological samples to aid cancer diagnosis, however multiple limitations occur using these methods for the diagnosis of thyroid cancer (TC) and non-small cell lung cancer (NSCLC). Fine needle aspiration cytology (FNAC) is the prominent diagnostic method used for the initial investigation of thyroid nodules but is limited by the inability to accurately diagnose malignancy in follicular-patterned lesion. As a result, more than 20% of cases under investigation for TC are classified as cytologically “indeterminate”, requiring surgical resection for accurate diagnosis. In the case of NSCLC, with the advent of targeted therapies, it is imperative to accurately differentiate (NSCLC) subtypes in order to ensure efficacy of treatment for patients. Immunohistochemistry and molecular techniques are increasingly part of the diagnostic work-up of NSCLC patients however due to the limitation of small sample size, overlapping morphological features and molecular characterisation, differential diagnosis of NSCLC still proves challenging. Raman spectroscopy has shown promising results for the detection of a variety of cancers, however to date there has been no evaluation of Raman spectroscopy on cytology bronchoscopy samples or thyroid FNAC samples, which may eliminate the limitations of current methods. This thesis explores the use of Raman spectroscopy as an alternative or adjunct tool for the diagnosis of TC and NSCLC using cytological specimens

Artificial intelligence and thyroid disease management: considerations for thyroid function tests

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design. Our article is reviewing several of these aspects. As thyroid AI models rely on large data sets, which often requires distributed learning from multi-center contributions, this article also briefly discusses this issue

Telomerase related studies in thyroid cancer

Follicular thyroid neoplasms are diagnostically challenging. On histologic evaluation, it can be difficult, resource-consuming, and observer-dependent to pinpoint the exact location of capsular or vascular invasion. In some cases, it is impossible to do so unequivocally – for those, the term “follicular tumor of uncertain malignant potential” (FT-UMP) was created. On cytologic evaluation, it is less challenging but rather hardly possible to distinguish follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC). With the advent of molecular analyses in clinical diagnostic settings, many mutational events have been associated with specific cancers. Amongst those, two point mutations in the TERT promoter region, named C228T and C250T have been of particular interest as they have been associated with malignant properties in thyroid tumors in general and a worse prognosis with a higher frequency of relapse in particular. This thesis aims to improve the diagnostic accuracy for thyroid tumors in general and for follicular thyroid tumors in particular through the implementation of TERT promoter mutational screening. Study I evaluates the role of TERT promoter mutational screening in a clinical series of FT-UMPs and how this analysis aids in detecting relapse-prone tumors. This could help alter adjuvant treatment modalities even in the absence of clearcut histopathological evidence of malignant potential. Study II shows that digital droplet PCR (ddPCR) can improve the sensitivity for the detection of TERT promoter mutations in follicular thyroid tumors and can even detect TERT promoter mutations when they occur subclonal and are heterogeneously distributed in FT-UMPs. Study III validates TERT promoter mutational testing on preoperative material in the form of frozen pellets from thyroid FNAC material. We were able to show that ddPCR is a reliable analysis for cytologic material and may help to identify high-risk cases and triage them to a more aggressive treatment plan up-front, underlining the markers' diagnostic and prognostic value. Study IV tries to evaluate 5hmC immunoreactivity as an expressional analysis to pinpoint TERT promoter mutations in FTCs. Even though the study was able to show that the loss of 5hmC immunoreactivity may signify TERT promoter mutations in subsets of FTCs, we could not prove its clinical value to predict the TERT promoter mutational status. Further studies are therefore warranted. In summary, the findings in this thesis highlight the clinical importance of TERT promoter mutational screening in follicular thyroid neoplasms. Furthermore, we were able to show that ddPCR is a reliable technique for interrogating specific mutations of the TERT promoter