Thyroid cancer is one of the most common cancers, with a global increase in incidence rate for both genders. Ultrasound-guided fine-needle aspiration is the current gold standard to diagnose thyroid cancers, but the results are inaccurate, leading to repeated biopsies and unnecessary surgeries. To reduce the number of unnecessary biopsies, we explored the use of multiparametric photoacoustic (PA) analysis in combination with the American Thyroid Association (ATA) Guideline (ATAP). In this study, we performed in vivo multispectral PA imaging on thyroid nodules from 52 patients, comprising 23 papillary thyroid cancer (PTC) and 29 benign cases. From the multispectral PA data, we calculated hemoglobin oxygen saturation level in the nodule area, then classified the PTC and benign nodules with multiparametric analysis. Statistical analyses showed that this multiparametric analysis of multispectral PA responses could classify PTC nodules. Combining the photoacoustically indicated probability of PTC and the ATAP led to a new scoring method that achieved a sensitivity of 83% and a specificity of 93%. This study is the first multiparametric analysis of multispectral PA data of thyroid nodules with statistical significance. As a proof of concept, the results show that the proposed new ATAP scoring can help physicians examine thyroid nodules for fine-needle aspiration biopsy, thus reducing unnecessary biopsies. Significance: This report highlights a novel photoacoustic scoring method for risk stratification of thyroid nodules, where malignancy of the nodules can be diagnosed with 83% sensitivity and 93% specificity.

artilage defects due to trauma, degenerative arthritis, or inflammatory arthritis affect approximately one out of five adults and represent a major cause of pain and disability (1-3). Because cartilage defects do not heal spontaneously, interventions are needed to induce repair. Bone marrow-derived autologous mesenchymal stromal cells (MSCs) can differentiate into chondrocytes and have been implanted into cartilage defects to restore joint health (4). However, cartilage repair outcomes of matrix-associated stem cell implants (MASIs) in patients have been highly variable: While some investigators reported full-thickness hyaline cartilage regeneration (5,6), others reported a failure rate of up to 50% for MASIs (7,8). Limited cell transplant survival was identified as the most important obstacle for successful cartilage repair (9). An imaging test that could help predict MASI outcomes would greatly enhance our ability to develop more successful cell transplant procedures. MRI is the primary modality for cartilage imaging (10,11). However, MRI within the 1st few weeks after MASI cannot help distinguish between grafts that will and grafts that will not repair the underlying cartilage defect (9). To date, successful cartilage repair is diagnosed many months after MASI, on the basis of a reduction in cartilage defect size at morphologic MRI (10,11). Unfortunately, failed cartilage repair and scar formation are difficult to correct at that time. Timely detection of an impending graft failure could enable rescue interventions

Background and Purpose: Imaging is frequently used to select acute stroke patients for intraarterial treatment (IAT). Quantitative cerebral blood flow (CBF) can be measured non-invasively with arterial spin labeling (ASL) magnetic resonance imaging (MRI). CBF levels in the contralateral (unaffected) hemisphere may affect capacity for collateral flow and patient outcome. The goal of this study was to determine whether higher contralateral CBF (cCBF) in acute stroke identifies patients with better 90-day functional outcome. Methods: Patients were part of the prospective, multicenter 'Imaging Collaterals in Acute Stroke' (iCAS) study between 2013 and 2017. Consecutive patients were enrolled after being diagnosed with anterior circulation acute ischemic stroke. Inclusion criteria were ischemic anterior circulation stroke, baseline National Institutes of Health Stroke Scale (NIHSS)>=1, pre-stroke modified Rankin Score (mRS)<=2, onset-to-imaging-time (OIT) <24 hrs, with imaging including diffusionweighted imaging (DWI) and ASL. Patients were dichotomized into high and low cCBF groups based on median cCBF. Outcomes were assessed by day 1 and 5 NIHSS; and day 30 and 90 mRS. Multivariable logistic regression was used to test whether cCBF predicted good neurological outcome (mRS 0-2) at 90 days. Results: Seventy-seven patients (41 female) met the inclusion criteria with median (inter-quartile range) age 66 (55-76) yrs, OIT 4.8 (3.6-7.7) hrs, and baseline NIHSS 13 (9-20). Median cCBF was 38.9 (31.2-44.5) ml/100g/min. Higher cCBF predicted good outcome at day 90 (OR 4.6, 95% CI 1.4-14.7, p=0.01), after controlling for baseline NIHSS, DWI lesion volume, and intra-arterial treatment. 3 Conclusion: Higher quantitative contralateral CBF at baseline is a significant predictor of good neurological outcome at day 90. cCBF levels may inform decisions regarding stroke triage, treatment of acute stroke, and general outcome prognosis.

As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting model misfit. The purposes of this study were to extend the use of RISE to more general and comprehensive applications by manipulating a variety of factors (e.g., test length, sample size, IRT models, ability distribution). The results from the simulation study demonstrated that RISE outperformed G 2 and S-X 2 in that it controlled Type I error rates and provided adequate power under the studied conditions. In the empirical study, RISE detected reasonable numbers of misfitting items compared to G 2 and S-X 2 , and RISE gave a much clearer picture of the location and magnitude of misfit for each misfitting item. In addition, there was no practical consequence to classification before and after replacement of misfitting items detected by three fit statistics.

Investigating the fit of a parametric model is an important part of the measurement process when implementing item response theory (IRT), but research examining it is limited. A general nonparametric approach for detecting model misfit, introduced by J. Douglas and A. S. Cohen (2001), has exhibited promising results for the two-parameter logistic model and Samejima s graded response model. This study extends this approach to test the fit of generalized partial credit model (GPCM). The empirical Type I error rate and power of the proposed method are assessed for various test lengths, sample sizes, and type of assessment. Overall, the proposed fit statistic performed well under the studied conditions in that the Type I error rate was not inflated and the power was acceptable, especially for moderate to large sample sizes. A further advantage of the nonparametric approach is that it provides a convenient graphical display of possible misfit.