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BackgroundIdentification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obtain a diagnosis despite most being non-cancerous. It is highly desirable to avoid surgery and to provide accurate classification of benignity versus malignancy from FNAB preoperatively. In our first-generation algorithm, Gene Expression Classifier (GEC), we achieved this goal by using machine learning (ML) on gene expression features. The classifier is sensitive, but not specific due in part to the presence of non-neoplastic benign Hürthle cells in many FNAB.ResultsWe sought to overcome this low-specificity limitation by expanding the feature set for ML using next-generation whole transcriptome RNA sequencing and called the improved algorithm the Genomic Sequencing Classifier (GSC). The Hürthle identification leverages mitochondrial expression and we developed novel feature extraction mechanisms to measure chromosomal and genomic level loss-of-heterozygosity (LOH) for the algorithm. Additionally, we developed a multi-layered system of cascading classifiers to sequentially triage Hürthle cell-containing FNAB, including: 1. presence of Hürthle cells, 2. presence of neoplastic Hürthle cells, and 3. presence of benign Hürthle cells. The final Hürthle cell Index utilizes 1048 nuclear and mitochondrial genes; and Hürthle cell Neoplasm Index leverages LOH features as well as 2041 genes. Both indices are Support Vector Machine (SVM) based. The third classifier, the GSC Benign/Suspicious classifier, utilizes 1115 core genes and is an ensemble classifier incorporating 12 individual models.ConclusionsThe accurate algorithmic depiction of this complex biological system among Hürthle subtypes results in a dramatic improvement of classification performance; specificity among Hürthle cell neoplasms increases from 11.8% with the GEC to 58.8% with the GSC, while maintaining the same sensitivity of 89%.

The newly introduced pathologic diagnosis of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) will result in less bilateral thyroid surgery as well as deescalation in T4 suppressive and radioactive iodine treatment. Although, NIFTP is a nonmalignant lesion that has nuclear features of some papillary malignancies, the challenge for the surgeon is to identify a lesion as possibly NIFTP before the pathologic diagnosis. NIFTP, due to its reduction of overall rates of malignancy, will result in the initial surgical pendulum swinging toward lobectomy instead of initial total thyroidectomy. This American Head and Neck Society endocrine section consensus statement is intended to inform preoperative evaluation to attempt to identify those patients whose final pathology report may ultimately harbor NIFTP and can be offered a conservative surgical plan to assist in cost-effective, optimal management of patients with NIFTP.

ImportanceUse of next-generation sequencing of RNA and machine learning algorithms can classify the risk of malignancy in cytologically indeterminate thyroid nodules to limit unnecessary diagnostic surgery.ObjectiveTo measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules.Design, setting, and participantsA blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine-needle aspiration biopsy between June 2009 and December 2010 from 49 academic and community centers in the United States. All patients underwent surgery without genomic information and were assigned a histopathology diagnosis by an expert panel blinded to all genomic information. There were 210 potentially eligible thyroid biopsy samples with Bethesda III or IV indeterminate cytopathology that constituted a cohort previously used to validate the gene expression classifier. Of these, 191 samples (91.0%) had adequate residual RNA for validation of the genomic sequencing classifier. Algorithm development and independent validation occurred between August 2016 and May 2017.ExposuresThyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data.Main outcomes and measuresThe primary end point was measurement of genomic sequencing classifier sensitivity, specificity, and negative and positive predictive values in biopsies from Bethesda III and IV nodules. The secondary end point was measurement of classifier performance in biopsies from Bethesda II, V, and VI nodules.ResultsOf the 183 included patients, 142 (77.6%) were women, and the mean (range) age was 51.7 (22.0-85.0) years. The genomic sequencing classifier had a sensitivity of 91% (95% CI, 79-98) and a specificity of 68% (95% CI, 60-76). At 24% cancer prevalence, the negative predictive value was 96% (95% CI, 90-99) and the positive predictive value was 47% (95% CI, 36-58).Conclusions and relevanceThe genomic sequencing classifier demonstrates high sensitivity and accuracy for identifying benign nodules. Its 36% increase in specificity compared with the gene expression classifier potentially increases the number of patients with benign nodules who can safely avoid unnecessary diagnostic surgery.

ObjectiveEvaluate the efficacy and safety/tolerability of cryoneurolysis for reduction of pain and symptoms associated with knee osteoarthritis (OA).DesignRandomized, double-blind, sham-controlled, multicenter trial with a 6-month follow-up in patients with mild-to-moderate knee OA. Patients were randomized 2:1 to cryoneurolysis targeting the infrapatellar branch of the saphenous nerve (IPBSN) or sham treatment. The primary endpoint was the change from baseline to Day 30 in the Western Ontario and McMaster Osteoarthritis Index (WOMAC) pain score adjusted by the baseline score and site. Secondary endpoints, including visual analogue scale (VAS) pain score and total WOMAC score, were tested in a pre-defined order.ResultsThe intent-to-treat (ITT) population consisted of 180 patients (n = 121 active treatment, n = 59 sham treatment). Compared to the sham group, patients who received active treatment had a statistically significant greater change from baseline in the WOMAC pain subscale score at Day 30 (P = 0.0004), Day 60 (P = 0.0176), and Day 90 (P = 0.0061). Patients deemed WOMAC pain responders at Day 120 continued to experience a statistically significant treatment effect at Day 150. Most expected side effects were mild in severity and resolved within 30 days. The incidence of device- or procedure-related adverse events was similar in the two treatment groups with no occurrence of serious or unanticipated adverse device effects (ADE).ConclusionsCryoneurolysis of the IPBSN resulted in statistically significant decreased knee pain and improved symptoms compared to sham treatment for up to 150 days, and appeared safe and well tolerated.

Context: The Afirma® GSC aids in risk stratifying indeterminate thyroid nodule cytology (ITN). The 2018 GSC validation study (VS) reported a sensitivity (SN) of 91%, specificity (SP) of 68%, positive predictive value (PPV) of 47%, and negative predictive value (NPV) of 96%. Since then, 13 independent real-world (RW) postvalidation studies have been published. Objective: This study's objective is to compare the RW GSC performance to the VS metrics. Methods: Rules and assumptions applying to this analysis include: (1) At least 1 patient with molecular benign results must have surgery for that study to be included in SN, SP, and NPV analyses. (2) Molecular benign results without surgical histology are considered true negatives (TN) (as are molecular benign results with benign surgical histology). (3) Unoperated patients with suspicious results are either excluded from analysis (observed PPV [oPPV] and observed SP [oSP]) or assumed histology negatives (false positives; conservative PPV [cPPV] and conservative SP [cSP]) 4. Noninvasive follicular thyroid neoplasm with papillary-like nuclear features is considered malignant. Results: In RW studies, the GSC demonstrates a SN, oSP, oPPV, and NPV of 97%, 88%, 65%, 99% respectively, and conservative RW performance showed cSP of 80% and cPPV of 49%, all significantly higher than the VS except for SN and cPPV. There was also a higher benign call rate (BCR) of 67% in RW studies compared to 54% in the VS (P<0.05). Conclusion: RW data for the Afirma GSC demonstrates significantly better oSP and oPPV performance than the VS, indicating an increased yield of cancers for resected GSC suspicious nodules. The higher BCR likely increases the overall rate of clinical observation in lieu of surgery. [ABSTRACT FROM AUTHOR]

This article identifies the predictors of child poverty rates at the state level before and after the adoption and implementation of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996. The analysis shows that the most important state-level factors that influence child poverty rates are demographics, the health and viability of the state economy, and often the generosity, inclusiveness, and quality of state welfare programs. States with large numbers of black citizens, and those that score highest on infant mortality, teen births, births to unmarried women, children living with a parent without a high school degree, and children living with a single parent have the highest rates of child poverty. Child poverty rates are lowest in states that suffer less unemployment, and in wealthier states. States that score higher on per capita personal income, tax revenues, and taxable resources have lower child poverty rates. While specific 'tough' welfare policies adopted by some states seem to have no impact on child poverty rates, we tested for the first time a sophisticated measure of the overall quality of state welfare programs. The analysis reveals that the global quality of a state's welfare programs is often an independent predictor of child poverty. States with the most generous, inclusive, and supportive welfare programs have done the best job of lowering and containing child poverty. KEY WORDS: poverty, child poverty, TANF, The passage of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA) ushered in a new approach to American welfare (Blank, 2002a; Harvard Law Review, 1996; Lieberman & [...]

This article uses four models to identify the best predictors of state poverty levels and changes in state poverty rates since the implementation of welfare reform. The policy decisions based on the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) by the states are tested along with more traditional variables identified in the literature. Using several measures of state poverty, the analysis finds that those states with the lowest poverty rates are those with the healthiest economies, and the most generous state spending on Temporary Assistance for Needy Families (TANF). States with the highest percentage of their TANF rolls made up of black citizens tend to have the highest rates of poverty. Initial poverty rates are found to be important in that states with higher initial poverty levels are capable of generating a larger reduction in poverty than states with lower initial rates. With the exception of limited evidence on sanctions, none of the PRWORA-based policies were found to have any effect on poverty rates., In 1996 Congress passed, and President Clinton signed, the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA). PRWORA is the most comprehensive reform of American welfare since the Social Security [...]

State spending on Temporary Assistance to Needy Families (TANF) greatly varies. Combined federal and state spending by the states per TANF family or recipient reflects the historic level of state generosity for Aid to Families with Dependent Children (AFDC), the failure of the federal government to set any minimum spending standard for the states, and the failure of the federal government to adjust federal grants for huge changes in state TANF caseloads. Our multivariate analysis shows that state spending for TANF is greatly influenced by the percentage of the state population that is black, the percentage of the state population that is on TANF (especially if a significant percentage of the rolls consist of black recipients), and the economic conditions within the state. Some states spend as much as their economies will allow, while other states spend far below capacity. Despite the very different goals of TANF, state spending is still heavily influenced by their historic approach to AFDC., In 1996 Congress created a new welfare system by passing the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA). One provision of this act repealed Aid to Families with Dependent [...]

Considerable literature finds that spending levels are important predictors of poverty rates, both in the American states and in western European nations. Those jurisdictions with the most generous support programs generally have lower poverty rates. This article examines state Temporary Assistance for Needy Families (TANF) spending levels in relation to national norms, the spending ability of the state as predicted by their total taxable revenue base, and the state's poverty rate. The analysis shows enormous variation in state per capita TANF spending, with many states spending far less than the national mean and their fiscal capacity. Most of the low-spending states have the fiscal ability to fund more vigorous programs but at optimal levels; some would still fall below the national mean. In return for improved funding, these states would be candidates for increased federal assistance. A few states with a high poverty rate and usually with large numbers of poor citizens are generous spenders, indicating that funding is one component of effective programs., Under the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) of 1996, Aid to Families with Dependent Children (AFDC) was replaced by Temporary Assistance to Needy Families (TANF). As opposed [...]