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In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus. [ABSTRACT FROM AUTHOR]

ObjectiveWe hypothesize that radiologists' estimated percentage likelihood assessments for the presence of ductal carcinoma in situ (DCIS) and invasive cancer may predict histologic outcomes.Materials and methodsTwo hundred fifty cases categorized as BI-RADS category 4 or 5 at four University of California Medical Centers were retrospectively reviewed by 10 academic radiologists with a range of 1-39 years in practice. Readers assigned BI-RADS category (1, 2, 3, 4a, 4b, 4c, or 5), estimated percentage likelihood of DCIS or invasive cancer (0-100%), and confidence rating (1 = low, 5 = high) after reviewing screening and diagnostic mammograms and ultrasound images. ROC curves were generated.ResultsSixty-two percent (156/250) of lesions were benign and 38% (94/250) were malignant. There were 26 (10%) DCIS, 20 (8%) invasive cancers, and 48 (19%) cases of DCIS and invasive cancer. AUC values were 0.830-0.907 for invasive cancer and 0.731-0.837 for DCIS alone. Sensitivity of 82% (56/68), specificity of 84% (153/182), positive predictive value (PPV) of 66% (56/85), negative predictive value (NPV) of 93% (153/165), and accuracy of 84% ([56 + 153]/250) were calculated using an estimated percentage likelihood of 20% or higher as the prediction threshold for invasive cancer for the radiologist with the highest AUC (0.907; 95% CI, 0.864-0.951). Every 20% increase in the estimated percentage likelihood of invasive cancer increased the odds of invasive cancer by approximately two times (odds ratio, 2.4). For DCIS, using a threshold of 40% or higher, sensitivity of 81% (21/26), specificity of 79% (178/224), PPV of 31% (21/67), NPV of 97% (178/183), and accuracy of 80% ([21 + 178]/250) were calculated. Similarly, these values were calculated at thresholds of 2% or higher (BI-RADS category 4) and 95% or higher (BI-RADS category 5) to predict the presence of malignancy.ConclusionUsing likelihood estimates, radiologists may predict the presence of invasive cancer with fairly high accuracy. Radiologist-assigned estimated percentage likelihood can predict the presence of DCIS, albeit with lower accuracy than that for invasive cancer.

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In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using 'cellular automata', 'land' and 'urban' as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors. [ABSTRACT FROM AUTHOR]

Background Pharmacy-based case mix measures are an alternative source of information to the relatively scarce outpatient diagnoses data. But most published tools use national drug nomenclatures and offer no head-to-head comparisons between drugs-related and diagnoses-based categories. The objective of the study was to test the accuracy of drugs-based morbidity groups derived from the World Health Organization Anatomical Therapeutic Chemical Classification of drugs by checking them against diagnoses-based groups. Methods We compared drugs-based categories with their diagnoses-based analogues using anonymous data on 108,915 individuals insured with one of four companies. They were followed throughout 2005 and 2006 and hospitalized at least once during this period. The agreement between the two approaches was measured by weighted kappa coefficients. The reproducibility of the drugs-based morbidity measure over the 2 years was assessed for all enrollees. Results Eighty percent used a drug associated with at least one of the 60 morbidity categories derived from drugs dispensation. After accounting for inpatient under-coding, fifteen conditions agreed sufficiently with their diagnoses-based counterparts to be considered alternative strategies to diagnoses. In addition, they exhibited good reproducibility and allowed prevalence estimates in accordance with national estimates. For 22 conditions, drugs-based information identified accurately a subset of the population defined by diagnoses. Conclusions Most categories provide insurers with health status information that could be exploited for healthcare expenditure prediction or ambulatory cost control, especially when ambulatory diagnoses are not available. However, due to insufficient concordance with their diagnoses-based analogues, their use for morbidity indicators is limited. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: The diagnostic accuracy of a clinical test is typically evaluated by comparing the classification of individuals by the test with their classification by some diagnostic gold standard. The most popular indices of diagnostic accuracy are sensitivity, specificity, and positive and negative predictive values. However, these measures are affected by the fact that some diagnostic decisions will be correct by chance, resulting in values that differ widely between indices, and clinicians may be confused as to whether results indicate that a test is good or poor. In keeping with the principle in evidence-based practice that decisions should be based on evidence not guesswork, we might expect that measures of diagnostic accuracy should therefore be chance corrected. The objective of this article was to advocate this and draw attention to indices that achieve this requirement. Study Design and Setting: The principles underlying calculations of diagnostic accuracy are presented as a framework for understanding the problem and its solution. Results: Disparities between different indices of diagnostic accuracy may be resolved by adjusting them to correct for chance effects. This produces a pair of weighted 2×2 “diagnostic” kappa coefficients offering a number of theoretical and practical advantages. Conclusion: Routine use of weighted 2×2 kappa coefficients as indices of diagnostic accuracy is recommended. [Copyright &y& Elsevier]

Objective: We hypothesize that radiologists' estimated percentage likelihood assessments for the presence of ductal carcinoma in situ (DCIS) and invasive cancer may predict histologic outcomes., Materials and Methods: Two hundred fifty cases categorized as BI-RADS category 4 or 5 at four University of California Medical Centers were retrospectively reviewed by 10 academic radiologists with a range of 1-39 years in practice. Readers assigned BI-RADS category (1, 2, 3, 4a, 4b, 4c, or 5), estimated percentage likelihood of DCIS or invasive cancer (0-100%), and confidence rating (1 = low, 5 = high) after reviewing screening and diagnostic mammograms and ultrasound images. ROC curves were generated., Results: Sixty-two percent (156/250) of lesions were benign and 38% (94/250) were malignant. There were 26 (10%) DCIS, 20 (8%) invasive cancers, and 48 (19%) cases of DCIS and invasive cancer. AUC values were 0.830-0.907 for invasive cancer and 0.731-0.837 for DCIS alone. Sensitivity of 82% (56/68), specificity of 84% (153/182), positive predictive value (PPV) of 66% (56/85), negative predictive value (NPV) of 93% (153/165), and accuracy of 84% ([56 + 153]/250) were calculated using an estimated percentage likelihood of 20% or higher as the prediction threshold for invasive cancer for the radiologist with the highest AUC (0.907; 95% CI, 0.864-0.951). Every 20% increase in the estimated percentage likelihood of invasive cancer increased the odds of invasive cancer by approximately two times (odds ratio, 2.4). For DCIS, using a threshold of 40% or higher, sensitivity of 81% (21/26), specificity of 79% (178/224), PPV of 31% (21/67), NPV of 97% (178/183), and accuracy of 80% ([21 + 178]/250) were calculated. Similarly, these values were calculated at thresholds of 2% or higher (BI-RADS category 4) and 95% or higher (BI-RADS category 5) to predict the presence of malignancy., Conclusion: Using likelihood estimates, radiologists may predict the presence of invasive cancer with fairly high accuracy. Radiologist-assigned estimated percentage likelihood can predict the presence of DCIS, albeit with lower accuracy than that for invasive cancer.