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

Recent advances in managing differentiated thyroid cancer

The main clinical challenge in the management of thyroid cancer is to avoid over-treatment and over-diagnosis in patients with lower-risk disease while promptly identifying those patients with more advanced or high-risk disease requiring aggressive treatment. In recent years, novel clinical and molecular data have emerged, allowing the development of new staging systems, predictive and prognostic tools, and treatment approaches. There has been a notable shift toward more conservative management of low- and intermediate-risk patients, characterized by less extensive surgery, more selective use of radioisotopes (for both diagnostic and therapeutic purposes), and less intensive follow-up. Furthermore, the histologic classification; tumor, node, and metastasis (TNM) staging; and American Thyroid Association risk stratification systems have been refined, and this has increased the number of patients in the low- and intermediate-risk categories. There is now a need for new, prospective data to clarify how these changing practices will impact long-term outcomes of patients with thyroid cancer, and new follow-up strategies and biomarkers are still under investigation. On the other hand, patients with more advanced or high-risk disease have a broader portfolio of options in terms of treatments and therapeutic agents, including multitarget tyrosine kinase inhibitors, more selective BRAF or MEK inhibitors, combination therapies, and immunotherapy

Solving the single-track train scheduling problem via Deep Reinforcement Learning

Every day, railways experience small inconveniences, both on the network and the fleet side, affecting the stability of rail traffic. When a disruption occurs, delays propagate through the network, resulting in demand mismatching and, in the long run, demand loss. When a critical situation arises, human dispatchers distributed over the line have the duty to do their best to minimize the impact of the disruptions. Unfortunately, human operators have a limited depth of perception of how what happens in distant areas of the network may affect their control zone. In recent years, decision science has focused on developing methods to solve the problem automatically, to improve the capabilities of human operators. In this paper, machine learning-based methods are investigated when dealing with the train dispatching problem. In particular, two different Deep Q-Learning methods are proposed. Numerical results show the superiority of these techniques respect to the classical linear Q-Learning based on matrices.Comment: 12 pages, 4 figures (2 b&w

Interobserver agreement of various thyroid imaging reporting and data systems

Ultrasonography is the best available tool for the initial work-up of thyroid nodules. Substantial interobserver variability has been documented in the recognition and reporting of some of the lesion characteristics. A number of classification systems have been developed to estimate the likelihood of malignancy: several of them have been endorsed by scientific societies, but their reproducibility has yet to be assessed. We evaluated the interobserver variability of the AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS classification systems and the interobserver concordance in the indication to FNA biopsy. Two raters independently evaluated 1055 ultrasound images of thyroid nodules identified in 265 patients at multiple time points, in two separate sets (501 and 554 images). After the first set of nodules, a joint reading was performed to reach a consensus in the feature definitions. The interobserver agreement (Krippendorff alpha) in the first set of nodules was 0.47, 0.49, 0.49, 0.61, and 0.53, for AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS systems, respectively. The agreement for the indication to biopsy was substantial to near-perfect, being 0.73, 0.61, 0.75, 0.68, and 0.82, respectively (Cohen's kappa). For all systems, agreement on the nodules of the second set increased. Despite the wide variability in the description of single ultrasonographic features, the classification systems may improve the interobserver agreement, that further ameliorates after a specific training. When selecting nodules to be submitted to FNA biopsy, that is main purpose of these classifications, the interobserver agreement is substantial to almost perfect

Margin Optimal Classification Trees

In recent years there has been growing attention to interpretable machine learning models which can give explanatory insights on their behavior. Thanks to their interpretability, decision trees have been intensively studied for classification tasks, and due to the remarkable advances in mixed-integer programming (MIP), various approaches have been proposed to formulate the problem of training an Optimal Classification Tree (OCT) as a MIP model. We present a novel mixed-integer quadratic formulation for the OCT problem, which exploits the generalization capabilities of Support Vector Machines for binary classification. Our model, denoted as Margin Optimal Classification Tree (MARGOT), encompasses the use of maximum margin multivariate hyperplanes nested in a binary tree structure. To enhance the interpretability of our approach, we analyse two alternative versions of MARGOT, which include feature selection constraints inducing local sparsity of the hyperplanes. First, MARGOT has been tested on non-linearly separable synthetic datasets in 2-dimensional feature space to provide a graphical representation of the maximum margin approach. Finally, the proposed models have been tested on benchmark datasets from the UCI repository. The MARGOT formulation turns out to be easier to solve than other OCT approaches, and the generated tree better generalizes on new observations. The two interpretable versions are effective in selecting the most relevant features and maintaining good prediction quality

Is thyroid nodule location associated with malignancy risk?

PURPOSE: Nodules located in the upper pole of the thyroid may carry a greater risk for malignancy than those in the lower pole. We conducted a study to analyze the risk of malignancy of nodules depending on location. METHODS: The records of patients undergoing thyroid-nodule fine-needle aspiration cytology (FNAC) at an academic thyroid cancer unit were prospectively collected. The nodules were considered benign in cases of a benign histology or cytology report, and malignant in cases of malignant histology. Pathological findings were analyzed based on the anatomical location of the nodules, which were also scored according to five ultrasonographic classification systems. RESULTS: Between November 1, 2015 and May 30, 2018, 832 nodules underwent FNAC, of which 557 had a definitive diagnosis. The prevalence of malignancy was not significantly different in the isthmus, right, or left lobe. Among the 227 nodules that had a precise longitudinal location noted (from 219 patients [155 females], aged 56.2±14.0 years), malignancy was more frequent in the middle lobe (13.2%; odds ratio [OR], 9.74; 95% confidence interval [CI], 1.95 to 48.59). This figure was confirmed in multivariate analyses that took into account nodule composition and the Thyroid Imaging, Reporting, and Data System (TIRADS) classification. Using the American College of Radiologists TIRADS, the upper pole location also demonstrated a slightly significant association with malignancy (OR, 6.92; 95% CI, 1.02 to 46.90; P=0.047). CONCLUSION: The risk of thyroid malignancy was found to be significantly higher for mid-lobar nodules. This observation was confirmed when suspicious ultrasonographic features were included in a multivariate model, suggesting that the longitudinal location in the lobe may be a risk factor independently of ultrasonographic appearance

Grey-scale analysis improves the ultrasonographic evaluation of thyroid nodules

Ultrasonography is the main imaging method for the workup of thyroid nodules. However, interobserver agreement reported for echogenicity and echotexture is quite low. The aim of this study was to perform quantitative measurements of the degree of echogenicity and heterogeneity of thyroid nodules, to develop an objective and reproducible method to stratify these features to predict malignancy.A retrospective study of patients undergoing ultrasonography-guided fine-needle aspiration was performed in an University hospital thyroid center. From January 2010 to October 2012, 839 consecutive patients (908 nodules) underwent US-guided fine-needle aspiration. In a single ultrasound image, 3 regions of interest (ROIs) were drawn: the first including the nodule; the second including a portion of the adjacent thyroid parenchyma; the third, the strap muscle. Histogram analysis was performed, expressing the median, mean, and SD of the gray levels of the pixels comprising each region. Echogenicity was expressed as a ratio: the nodule/parenchyma, the nodule/muscle, and parenchyma/muscle median gray ratios were calculated. The heterogeneity index (HI) was calculated as the coefficient of variation of gray histogram for each of the 3 ROIs. Cytology and histology reports were recorded.Nodule/parenchyma median gray ratio was significantly lower (more hypoechoic) in nodules found to be malignant (0.45 vs 0.61; P = 0.002) and can be used as a continuous measure of hypoechogenicity (odds ratio [OR] 0.12; 95% confidence interval [CI] 0.03-0.49). Using a cutoff derived from ROC curve analysis (<0.46), it showed a substantial inter-rater agreement (k = 0.74), sensitivity of 56.7% (95% CI 37.4-74.5%), specificity of 72.0% (67.8-75.9%), positive likelihood ratio (LR) of 2.023 (1.434-2.852), and negative LR of 0.602 (0.398-0.910) in predicting malignancy (diagnostic odds ratio 3.36; 1.59-7.10). Parenchymal HI was associated with anti-thyroperoxidase positivity (OR 19.69; 3.69-105.23). The nodule HI was significantly higher in malignant nodules (0.73 vs 0.63; P = 0.03) and, if above the 0.60 cutoff, showed sensitivity of 76.7% (57.7-90.1%), specificity of 46.8% (42.3-51.4%), positive LR of 1.442 (1.164-1.786), and negative LR of 0.498 (0.259-0.960).Evaluation of nodule echogenicity and echotexture according to a numerical estimate (nodule/parenchyma median gray ratio and nodule HI) allows for an objective stratification of nodule echogenicity and internal structure

Il ruolo dello screening per il carcinoma differenziato della tiroide in rapporto al sesso e in specifiche popolazioni

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User Involvement in the Design of ML-Infused Systems

Advances in machine learning (ML) open up possibilities for better supporting the decision making that occurs in high-stakes domains such as air traffic management (ATM). The success of such decision-making systems highly depends upon end users’ involvement in their development process. However, most designers face challenges with finding appropriate ways of doing this. This paper presents our ongoing work to investigate design practices by reporting lessons learned from user involvement in the development of an ML-infused ATM decision support system. To explore if and how UX design methods need to be refined when working with ML as a design material, we conducted an online study with domain experts consisting of three iterations. The paper reports the main challenges we faced and our actions to overcome them. Our results can be useful to other designers working with ML-infused systems.acceptedVersio

The ultrasound risk stratification systems for thyroid nodule have been evaluated against papillary carcinoma: a meta-analysis

Thyroid imaging reporting and data systems (TIRADS) are used to stratify the malignancy risk of thyroid nodule by ultrasound (US) examination. We conducted a meta-analysis to evaluate the pooled cancer prevalence and the relative prevalence of papillary, medullary, follicular thyroid cancer (PTC, MTC, and FTC) and other malignancies among nodules included in studies evaluating their performance. Four databases were searched until February 2020. Original articles with at least 1000 nodules, evaluating the performance of at least one TIRADS among AACE/ACE/AME, ACR-TIRADS, ATA, EU-TIRADS, or K-TIRADS, and reporting data on the histological diagnosis of malignant lesions were included. The number of malignant nodules, PTC, FTC, MTC and other malignancies in each study was extracted. For statistical pooling of data, a random-effects model was used. Nine studies were included, evaluating 19,494 thyroid nodules. The overall prevalence of malignancy was 34% (95%CI 21 to 49). Among 6162 histologically proven malignancies, the prevalence of PTC, FTC, MTC and other malignancies was 95%, 2%, 1%, and 1%, respectively. A high heterogeneity was found for all the outcomes. A limited number of studies generally conducted using a retrospective design was found, with possible selection bias. Acknowledging this limitation, TIRADSs should be regarded as accurate tools to diagnose PTC only. Proposed patterns and/or cut-offs should be revised and other strategies considered to improve their performance in the assessment of FTC, MTC and other malignancies