Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT.

OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network. METHODS: This retrospective study included consecutive patients with colorectal adenocarcinoma who underwent abdominal CT before CRC resection surgery (training set = 379, test set = 103). We customized the 3D U-Net of nnU-Net (CUNET) for CRC detection, which was trained with fivefold cross-validation using annotated CT images. CUNET was validated using datasets covering various clinical situations and institutions: an internal test set (n = 103), internal patients with CRC first determined by CT (n = 54) and asymptomatic CRC (n = 51), and an external validation set from two institutions (n = 60). During each validation, data from the healthy population were added (internal = 60; external = 130). CUNET was compared with other deep CNNs: residual U-Net and EfficientDet. The CAD performances were evaluated using per-CRC sensitivity (true positive/all CRCs), free-response receiver operating characteristic (FROC), and jackknife alternative FROC (JAFROC) curves. RESULTS: CUNET showed a higher maximum per-CRC sensitivity than residual U-Net and EfficientDet (internal test set 91.3% vs. 61.2%, and 64.1%). The per-CRC sensitivity of CUNET at false-positive rates of 3.0 was as follows: internal CRC determined by CT, 89.3%; internal asymptomatic CRC, 87.3%; and external validation, 89.6%. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 89.7% (252/281) of CRCs from all validation sets. CONCLUSIONS: CUNET can detect CRC on abdominal CT in patients with various clinical situations and from external institutions. KEY POINTS: • Customized 3D U-Net of nnU-Net (CUNET) can be applied to the opportunistic detection of colorectal cancer (CRC) in abdominal CT, helping radiologists detect unexpected CRC. • CUNET showed the best performance at false-positive rates ≥ 3.0, and 30.1% of false-positives were in the colorectum. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 87.3% (48/55) of asymptomatic CRCs. • CUNET detected CRCs in multiple validation sets composed of varying clinical situations and from different institutions, and CUNET detected 89.7% (252/281) of CRCs from all validation sets.

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging.

BACKGROUND: Deep learning (DL) algorithms are playing an increasing role in automatic medical image analysis. PURPOSE: To evaluate the performance of a DL model for the automatic detection of intracranial haemorrhage and its subtypes on non-contrast CT (NCCT) head studies and to compare the effects of various preprocessing and model design implementations. METHODS: The DL algorithm was trained and externally validated on open-source, multi-centre retrospective data containing radiologist-annotated NCCT head studies. The training dataset was sourced from four research institutions across Canada, the USA and Brazil. The test dataset was sourced from a research centre in India. A convolutional neural network (CNN) was used, with its performance compared against similar models with additional implementations: (1) a recurrent neural network (RNN) attached to the CNN, (2) preprocessed CT image-windowed inputs and (3) preprocessed CT image-concatenated inputs. The area under the receiver operating characteristic curve (AUC-ROC) and microaveraged precision (mAP) score were used to evaluate and compare model performances. RESULTS: The training and test datasets contained 21,744 and 491 NCCT head studies, respectively, with 8,882 (40.8%) and 205 (41.8%) positive for intracranial haemorrhage. Implementation of preprocessing techniques and the CNN-RNN framework increased mAP from 0.77 to 0.93 and increased AUC-ROC [95% confidence intervals] from 0.854 [0.816-0.889] to 0.966 [0.951-0.980] (p-value = 3.91 × 10-12). CONCLUSIONS: The deep learning model accurately detected intracranial haemorrhage and improved in performance following specific implementation techniques, demonstrating clinical potential as a decision support tool and an automated system to improve radiologist workflow efficiency. KEY POINTS: • The deep learning model detected intracranial haemorrhages on computed tomography with high accuracy. • Image preprocessing, such as windowing, plays a large role in improving deep learning model performance. • Implementations which enable an analysis of interslice dependencies can improve deep learning model performance. • Visual saliency maps can facilitate explainable artificial intelligence systems. • Deep learning within a triage system may expedite earlier intracranial haemorrhage detection.

Assessment of hepatic arterial hemodynamics with 4D flow MRI: in vitro analysis of motion and spatial resolution related error and in vivo feasibility study in 20 volunteers.

OBJECTIVES: To assess the ability of four-dimensional (4D) flow MRI to measure hepatic arterial hemodynamics by determining the effects of spatial resolution and respiratory motion suppression in vitro and its applicability in vivo with comparison to two-dimensional (2D) phase-contrast MRI. METHODS: A dynamic hepatic artery phantom and 20 consecutive volunteers were scanned. The accuracies of Cartesian 4D flow sequences with k-space reordering and navigator gating at four spatial resolutions (0.5- to 1-mm isotropic) and navigator acceptance windows (± 8 to ± 2 mm) and one 2D phase-contrast sequence (0.5-mm in -plane) were assessed in vitro at 3 T. Two sequences centered on gastroduodenal and hepatic artery branches were assessed in vivo for intra - and interobserver agreement and compared to 2D phase-contrast. RESULTS: In vitro, higher spatial resolution led to a greater decrease in error than narrower navigator window (30.5 to -4.67% vs -6.64 to -4.67% for flow). In vivo, hepatic and gastroduodenal arteries were more often visualized with the higher resolution sequence (90 vs 71%). Despite similar interobserver agreement (κ = 0.660 and 0.704), the higher resolution sequence had lower variability for area (CV = 20.04 vs 30.67%), flow (CV = 34.92 vs 51.99%), and average velocity (CV = 26.47 vs 44.76%). 4D flow had lower differences between inflow and outflow at the hepatic artery bifurcation (11.03 ± 5.05% and 15.69 ± 6.14%) than 2D phase-contrast (28.77 ± 21.01%). CONCLUSION: High-resolution 4D flow can assess hepatic artery anatomy and hemodynamics with improved accuracy, greater vessel visibility, better interobserver reliability, and internal consistency. KEY POINTS: • Motion-suppressed Cartesian four-dimensional (4D) flow MRI with higher spatial resolution provides more accurate measurements even when accepted respiratory motion exceeds voxel size. • 4D flow MRI with higher spatial resolution provides substantial interobserver agreement for visualization of hepatic artery branches. • Lower peak and average velocities and a trend toward better internal consistency were observed with 4D flow MRI as compared to 2D phase-contrast.

Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT.

OBJECTIVES: To develop and evaluate a deep learning-based algorithm (DLA) for automatic detection of bone metastases on CT. METHODS: This retrospective study included CT scans acquired at a single institution between 2009 and 2019. Positive scans with bone metastases and negative scans without bone metastasis were collected to train the DLA. Another 50 positive and 50 negative scans were collected separately from the training dataset and were divided into validation and test datasets at a 2:3 ratio. The clinical efficacy of the DLA was evaluated in an observer study with board-certified radiologists. Jackknife alternative free-response receiver operating characteristic analysis was used to evaluate observer performance. RESULTS: A total of 269 positive scans including 1375 bone metastases and 463 negative scans were collected for the training dataset. The number of lesions identified in the validation and test datasets was 49 and 75, respectively. The DLA achieved a sensitivity of 89.8% (44 of 49) with 0.775 false positives per case for the validation dataset and 82.7% (62 of 75) with 0.617 false positives per case for the test dataset. With the DLA, the overall performance of nine radiologists with reference to the weighted alternative free-response receiver operating characteristic figure of merit improved from 0.746 to 0.899 (p < .001). Furthermore, the mean interpretation time per case decreased from 168 to 85 s (p = .004). CONCLUSION: With the aid of the algorithm, the overall performance of radiologists in bone metastases detection improved, and the interpretation time decreased at the same time. KEY POINTS: • A deep learning-based algorithm for automatic detection of bone metastases on CT was developed. • In the observer study, overall performance of radiologists in bone metastases detection improved significantly with the aid of the algorithm. • Radiologists' interpretation time decreased at the same time.

The impact of cardiopulmonary hemodynamic factors in volumetry for pulmonary nodule management.

BACKGROUND: The acceptance of coronary CT angiogram (CCTA) scans in the management of stable angina has led to an exponential increase in studies performed and reported incidental findings, including pulmonary nodules (PN). Using low-dose CT scans, volumetry tools are used in growth assessment and risk stratification of PN between 5 and 8 mm in diameter. Volumetry of PN could also benefit from the increased temporal resolution of CCTA scans, potentially expediting clinical decisions when an incidental PN is first detected on a CCTA scan, and allow for better resource management and planning in a Radiology department. This study aims to investigate how cardiopulmonary hemodynamic factors impact the volumetry of PN using CCTA scans. These factors include the cardiac phase, vascular distance from the main pulmonary artery (MPA) to the nodule, difference of the MPA diameter between systole and diastole, nodule location, and cardiomegaly presence. MATERIALS AND METHODS: Two readers reviewed all CCTA scans performed from 2016 to 2019 in a tertiary hospital and detected PN measuring between 5 and 8 mm in diameter. Each observer measured each nodule using two different software packages and in systole and diastole. A multiple linear regression model was applied, and inter-observer and inter-software agreement were assessed using intraclass correlation. RESULTS: A total of 195 nodules from 107 patients were included in this retrospective, cross-sectional and observational study. The regression model identified the vascular distance (p < 0.001), the difference of the MPA diameter between systole and diastole (p < 0.001), and the location within the lower or posterior thirds of the field of view (p < 0.001 each) as affecting the volume measurement. The cardiac phase was not significant in the model. There was a very high inter-observer agreement but no reasonable inter-software agreement between measurements. CONCLUSIONS: PN volumetry using CCTA scans seems to be sensitive to cardiopulmonary hemodynamic changes independently of the cardiac phase. These might also be relevant to non-gated scans, such as during PN follow-up. The cardiopulmonary hemodynamic changes are a new limiting factor to PN volumetry. In addition, when a patient experiences an acute or deteriorating cardiopulmonary disease during PN follow-up, these hemodynamic changes could affect the PN growth estimation.

Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications.

BACKGROUND: Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. METHODS: One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. RESULTS: Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). CONCLUSION: Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems.

Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity.

OBJECTIVE: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. MATERIALS AND METHODS: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. RESULTS: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). CONCLUSION: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.

OBJETIVO: Associar características radiômicas de lesões pulmonares em imagens de tomografia computadorizada com a sobrevida global de pacientes com câncer de pulmão. MATERIAIS E MÉTODOS: Estudo retrospectivo composto por 101 pacientes consecutivos com neoplasia maligna confirmada por biópsia/cirurgia. As lesões foram semiautomaticamente segmentadas e caracterizadas por 2.465 variáveis radiômicas. A avaliação prognóstica foi baseada na análise de Kaplan-Meier e no teste log-rank, de acordo com a mediana dos valores das variáveis. RESULTADOS: Vinte e oito pacientes faleceram (16 por câncer de pulmão) e 73 foram censurados, com tempo médio de sobrevida de 1.819,4 dias (intervalo de confiança 95% [IC 95%]: 1.481,2-2.157,5). Uma característica radiômica (média de Fourier) apresentou diferença nas curvas de Kaplan-Meier (p < 0,05). Um grupo de pacientes de maior risco foi identificado a partir de valores altos da variável: sobrevida de 1.465,4 dias (IC 95%: 985,2-1.945,6) e razão de risco de 2,12 (IC 95%: 1,01-4,48). Um grupo de menor risco foi identificado a partir de valores baixos da variável (sobrevida de 2.164,8 dias; IC 95%: 1.745,4-2.584,1). CONCLUSÃO: Este estudo apresentou uma assinatura radiômica em imagens de tomografia computadorizada, baseada na transformada de Fourier, correlacionada com a sobrevida global de pacientes com câncer de pulmão, representando assim um biomarcador prognóstico.

Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity / Análise radiômica do câncer de pulmão para avaliação prognóstica do paciente e da heterogeneidade intratumoral

Abstract Objective: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.

Resumo Objetivo: Associar características radiômicas de lesões pulmonares em imagens de tomografia computadorizada com a sobrevida global de pacientes com câncer de pulmão. Materiais e Métodos: Estudo retrospectivo composto por 101 pacientes consecutivos com neoplasia maligna confirmada por biópsia/cirurgia. As lesões foram semiautomaticamente segmentadas e caracterizadas por 2.465 variáveis radiômicas. A avaliação prognóstica foi baseada na análise de Kaplan-Meier e no teste log-rank, de acordo com a mediana dos valores das variáveis. Resultados: Vinte e oito pacientes faleceram (16 por câncer de pulmão) e 73 foram censurados, com tempo médio de sobrevida de 1.819,4 dias (intervalo de confiança 95% [IC 95%]: 1.481,2-2.157,5). Uma característica radiômica (média de Fourier) apresentou diferença nas curvas de Kaplan-Meier (p < 0,05). Um grupo de pacientes de maior risco foi identificado a partir de valores altos da variável: sobrevida de 1.465,4 dias (IC 95%: 985,2-1.945,6) e razão de risco de 2,12 (IC 95%: 1,01-4,48). Um grupo de menor risco foi identificado a partir de valores baixos da variável (sobrevida de 2.164,8 dias; IC 95%: 1.745,4-2.584,1). Conclusão: Este estudo apresentou uma assinatura radiômica em imagens de tomografia computadorizada, baseada na transformada de Fourier, correlacionada com a sobrevida global de pacientes com câncer de pulmão, representando assim um biomarcador prognóstico.

Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology.

OBJECTIVES: The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules. METHODS: One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves. RESULTS: Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583). CONCLUSIONS: Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.

Advances in clinical assessment of bone healing / 中华创伤骨科杂志

The process of bone healing is absolutely complicated and affected by a wide variety of factors. The quality of bone healing directly determines management approaches. Therefore, it is crucial to evaluate accurately outcomes of bone healing. The assessments of bone healing mostly used in current clinical practice are a combination of clinical manifestations and X-ray examination while computed tomography (CT) and ultrasound may be applied alternatively for particular parts and populations. As understanding of bone healing process and bone biomechanical structure is deepening in recent years, both traditional and novel assessments of bone healing have been well refined. This review will expound on the advantages, disadvantages and clinical indications of various assessments, as well as their future development trends, to provide useful information for clinicians.

Radiomic feature analysis of computed tomography images for the diagnosis of invasive pulmonary adenocarcinoma appearing as ground-glass nodules / 中国基层医药

Objective:To investigate the value of radiomic features of computed tomography (CT) images in the diagnosis of invasive pulmonary adenocarcinoma appearing as part-solid ground-glass nodules.Methods:The clinical data of 100 part-solid ground-glass nodules from 88 patients with pulmonary adenocarcinoma confirmed by pathological diagnosis who received surgical treatment in Taizhou Tumor Hospital, China between February 2016 and April 2019 were retrospectively analyzed. Among these 100 part-solid ground-glass nodules, 56 from 53 patients were diagnosed as invasive pulmonary adenocarcinoma and 44 from 35 patients as non-invasive pulmonary adenocarcinoma. A set of regular risk factors and visually-assessed qualitative CT imaging features were compared with the radiomic features using logistic regression analysis. Three diagnostic models, i.e., a basis model using the clinical risk factors and qualitative CT features, a radiomics model using significant radiomic features, and a nomogram model combining all significant features, were established and their diagnostic efficacy was compared based on receiver operating characteristic (ROC) curves. Decision curve analysis was performed for the nomogram model to explore its potential clinical benefit.Results:Multiple logistic regression analysis showed that three qualitative CT imaging features (pleural traction ( P = 0.006), solid component size ( P = 0.045) and solid component proportion ( P = 0.020)) and quantitative Rad score ( P = 0.046) were significantly correlated with invasive pulmonary adenocarcinoma. The adjusted ratios were 7.189, 0.075, 194.786 and 2.016, respectively. The diagnostic nomogram model based on these four features showed that the area under the ROC curve (AUC) was 0.903 (95% CI: 0.845, 0.975). The diagnostic nomogram model showed a significantly higher performance (AUC = 0.903) in differentiating invasive pulmonary adenocarcinoma from non-invasive pulmonary adenocarcinoma than either the basis model (AUC = 0.853, P = 0.000) or the radiomics model (AUC = 0.769, P < 0.001). Decision curve analysis indicated a potential benefit of using such a nomogram model in clinical diagnosis. Conclusion:Quantitative radiomic features provide additional information regarding clinically-assessed qualitative features for differentiating invasive pulmonary adenocarcinoma from non-invasive pulmonary adenocarcinoma appearing as ground-glass nodules, and a diagnostic nomogram model including all these significant features may be clinically useful in preoperative strategy planning.

Soft Tissue Cephalometric Measurements Among Malaysian Malays and Chinese

Abstract Objective: To obtain the standardized values of individuals of Malaysian Malay and Chinese for further relevant research, such as treatment planning and aesthetical considerations. Material and Methods: In this retrospective study, 440 (305 were Malays and 135 were Chinese) standardized lateral cephalometric radiographs of orthodontic patients selected through simple random sampling are profiled using Holdaway's analysis. The independent t-test was used to assess the disparities in race and gender. The significant level was p<0.05. Results: Significant differences were found between the Malays and Chinese in their skeletal profile convexity, superior sulcus depth, inferior sulcus to the H line and nose prominence. Between Malay females and males, there are significant differences in superior sulcus depth, soft tissue subnasale to H line, basic upper lip thickness, upper lip thickness and nose prominence. Between Chinese males and females, there were differences in their skeletal profile convexity, upper lip to H line, basic upper lip thickness and upper lip thickness. Conclusion: The findings demonstrated the difference between standardized norms and the unique profiles of Malaysian Malays and Chinese. There are significant gender disparities in the soft tissue cephalometric measurements among Malaysian Malay and Chinese subjects.

A study on modelling cochlear duct mid-scalar length based on high-resolution computed tomography, and its effect on peri-modiolar and mid-scalar implant selection.

OBJECTIVE: To determine cochlear duct mid-scalar length in normal cochleae and its role in selecting the correct peri-modiolar and mid-scalar implant length. METHODS: The study included 40 patients with chronic otitis media who underwent high-resolution computed tomography of the temporal bone. The length and height of the basal turn, mid-modiolar height of the cochlea, mid-scalar and lateral wall length of the cochlear duct, and the 'X' line (the largest distance from mid-point of the round window to the mid-scalar point of the cochlear canal) were measured. RESULTS: Cochlear duct lateral wall length (28.88 mm) was higher than cochlear duct mid-scalar length (20.08 mm) (p < 0.001). The simple linear regression equation for estimating complete cochlear duct length was: cochlear duct length = 0.2 + 2.85 × X line. CONCLUSION: Using the mid-scalar point as the reference point (rather than the lateral wall) for measuring cochlear duct mid-scalar length, when deciding on the length of mid-scalar or peri-modiolar electrode, increases measurement accuracy. Mean cochlear duct mid-scalar length was compatible with peri-modiolar and mid-scalar implant lengths. The measurement method described herein may be useful for pre-operative peri-modiolar or mid-scalar implant selection.

Risk assessment of hepatocellular carcinoma development for indeterminate hepatic nodules in patients with chronic hepatitis B.

BACKGROUND/AIMS: A risk prediction model for the development of hepatocellular carcinoma (HCC) from indeterminate nodules detected on computed tomography (CT) (RadCT score) in patients with chronic hepatitis B (CHB)-related cirrhosis was proposed. We validated this model for indeterminate nodules on magnetic resonance imaging (MRI). METHODS: Between 2013 and 2016, Liver Imaging Reporting and Data System (LI-RADS) 2/3 nodules on MRI were detected in 99 patients with CHB. The RadCT score was calculated. RESULTS: The median age of the 72 male and 27 female subjects was 58 years. HCC history and liver cirrhosis were found in 47 (47.5%) and 44 (44.4%) patients, respectively. The median RadCT score was 112. The patients with HCC (n=41, 41.4%) showed significantly higher RadCT scores than those without (median, 119 vs. 107; P=0.013); the Chinese university-HCC and risk estimation for HCC in CHB (REACH-B) scores were similar (both P>0.05). Arterial enhancement, T2 hyperintensity, and diffusion restriction on MRI were not significantly different in the univariate analysis (all P>0.05); only the RadCT score significantly predicted HCC (hazard ratio [HR]=1.018; P=0.007). Multivariate analysis showed HCC history was the only independent HCC predictor (HR=2.374; P=0.012). When the subjects were stratified into three risk groups based on the RadCT score (<60, 60-105, and >105), the cumulative HCC incidence was not significantly different among them (all P>0.05, log-rank test). CONCLUSION: HCC history, but not RadCT score, predicted CHB-related HCC development from LI-RADS 2/3 nodules. New risk models optimized for MRI-defined indeterminate nodules are required.

Machine Learning Algorithms Utilizing Functional Respiratory Imaging May Predict COPD Exacerbations.

RATIONALE AND OBJECTIVES: Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression. MATERIALS AND METHODS: A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms. RESULTS: A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline. CONCLUSION: This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.

Risk assessment of hepatocellular carcinoma development for indeterminate hepatic nodules in patients with chronic hepatitis B

BACKGROUND/AIMS: A risk prediction model for the development of hepatocellular carcinoma (HCC) from indeterminate nodules detected on computed tomography (CT) (Rad(CT) score) in patients with chronic hepatitis B (CHB)-related cirrhosis was proposed. We validated this model for indeterminate nodules on magnetic resonance imaging (MRI).METHODS: Between 2013 and 2016, Liver Imaging Reporting and Data System (LI-RADS) 2/3 nodules on MRI were detected in 99 patients with CHB. The Rad(CT) score was calculated.RESULTS: The median age of the 72 male and 27 female subjects was 58 years. HCC history and liver cirrhosis were found in 47 (47.5%) and 44 (44.4%) patients, respectively. The median Rad(CT) score was 112. The patients with HCC (n=41, 41.4%) showed significantly higher Rad(CT) scores than those without (median, 119 vs. 107; P=0.013); the Chinese university-HCC and risk estimation for HCC in CHB (REACH-B) scores were similar (both P>0.05). Arterial enhancement, T2 hyperintensity, and diffusion restriction on MRI were not significantly different in the univariate analysis (all P>0.05); only the Rad(CT) score significantly predicted HCC (hazard ratio [HR]=1.018; P=0.007). Multivariate analysis showed HCC history was the only independent HCC predictor (HR=2.374; P=0.012). When the subjects were stratified into three risk groups based on the Rad(CT) score (<60, 60–105, and >105), the cumulative HCC incidence was not significantly different among them (all P>0.05, log-rank test).CONCLUSIONS: HCC history, but not Rad(CT) score, predicted CHB-related HCC development from LI-RADS 2/3 nodules. New risk models optimized for MRI-defined indeterminate nodules are required.

Application of quantitative imaging technique in the diagnosis of pancreatic cancer / 中国基层医药

Pancreatic cancer is a malignant tumor,and the clinical manifestations are not characteristic,so it is difficult to diagnose at an early stage, and it has a poor prognosis. Image technology has been widely used in clinical,with mature technology,and the application of image technology not only confined to the image display,but also has become a hot spot of research, including dynamic contrast enhancement magnetic resonance imaging (DCE－MRI),intravoxel incoherent motion ( IVIM),CT perfusion imaging,which could analyze quantitatively the permeability and microvascular of lesions,to improve the sensitivity of diagnosis,and to assess quantitatively the effect of drugs,radiation and chemotherapy.

CORRELATION BETWEEN AHLBÄCK CLASSIFICATION AND GONARTHROSIS RISK FACTORS.

OBJECTIVE: To demonstrate whether or not there is a correlation between the risk factors for gonarthrosis and the radiographic classification of Ahlbäck. METHODS: We studied patients with primary gonarthrosis attended at the knee outpatient clinic of the General Hospital of Vila Penteado during their routine visit. We collected data on patient age (years), weight (kg), height (meters), body mass index (BMI = patient weight/height2), personal history of hypertension or diabetes mellitus (positive or negative), sedentarism (physical activity less than three times per week, 30 minutes per session), functional demand (how many blocks walked weekly), time of onset of symptoms (in years) and laterality or bilaterality. The data were correlated with the Ahlbäck classification applied to the radiographs performed at the time of the consultation. RESULTS: A sample of 108 patients was studied. We did not find an association between the Ahlbäck classification and the patient's age, smoking, sedentary lifestyle, laterality, number of blocks walked per week, diabetes mellitus, and sex; however, a positive association was observed in hypertensive patients as well as a weak correlation with height and weight of the patient and moderate correlation with BMI. CONCLUSION: The Ahlbäck classification is unrelated to most of the risk factors for primary gonarthrosis. Level of evidence III, Case-control study.

OBJETIVO: Demonstrar se existe ou não correlação entre os fatores de risco de gonartrose e a classificação radiográfica de Ahlbäck. MÉTODOS: Estudamos pacientes com gonartrose primária, assistidos no ambulatório de joelho do Hospital Geral de Vila Penteado em sua consulta de rotina. Foram coletados dados referentes a idade do paciente (anos), peso do paciente (kg), altura (metros), índice de massa corporal (IMC= peso do paciente/altura2), antecedente pessoal de hipertensão ou diabetes mellitus (positivo ou negativo), sedentarismo (se pratica atividade física menos de três vezes por semana, 30 minutos por sessão), demanda funcional (quantas quadras caminha semanalmente), tempo do início dos sintomas (em anos) e lateralidade ou bilateralidade. Os dados foram correlacionados com a classificação de Ahlbäck aplicada às radiografias realizadas no momento da consulta. RESULTADOS: Uma amostra de 108 pacientes foi estudada. Não encontramos associação entre a classificação de Ahlbäck e a idade do paciente, tabagismo, sedentarismo, lateralidade, quantidade de quadras percorridas por semana, diabetes mellitus e sexo do paciente, porém verificou-se associação positiva em pacientes hipertensos e correlação fraca com altura e peso do paciente e correlação moderada com IMC. CONCLUSÃO: A classificação de Ahlbäck não apresenta relação com a maioria dos fatores de risco de gonartrose primária. Nível de evidência III, Estudo caso-controle .

Correlation between ahlbäck classification and gonarthrosis risk factors / Correlação entre a classificacão de ahlbäck e fatores de risco da gonartrose

ABSTRACT Objective: To demonstrate whether or not there is a correlation between the risk factors for gonarthrosis and the radiographic classification of Ahlbäck. Methods: We studied patients with primary gonarthrosis attended at the knee outpatient clinic of the General Hospital of Vila Penteado during their routine visit. We collected data on patient age (years), weight (kg), height (meters), body mass index (BMI = patient weight/height2), personal history of hypertension or diabetes mellitus (positive or negative), sedentarism (physical activity less than three times per week, 30 minutes per session), functional demand (how many blocks walked weekly), time of onset of symptoms (in years) and laterality or bilaterality. The data were correlated with the Ahlbäck classification applied to the radiographs performed at the time of the consultation. Results: A sample of 108 patients was studied. We did not find an association between the Ahlbäck classification and the patient's age, smoking, sedentary lifestyle, laterality, number of blocks walked per week, diabetes mellitus, and sex; however, a positive association was observed in hypertensive patients as well as a weak correlation with height and weight of the patient and moderate correlation with BMI. Conclusion: The Ahlbäck classification is unrelated to most of the risk factors for primary gonarthrosis. Level of evidence III, Case-control study.

RESUMO Objetivo: Demonstrar se existe ou não correlação entre os fatores de risco de gonartrose e a classificação radiográfica de Ahlbäck. Métodos: Estudamos pacientes com gonartrose primária, assistidos no ambulatório de joelho do Hospital Geral de Vila Penteado em sua consulta de rotina. Foram coletados dados referentes a idade do paciente (anos), peso do paciente (kg), altura (metros), índice de massa corporal (IMC= peso do paciente/altura2), antecedente pessoal de hipertensão ou diabetes mellitus (positivo ou negativo), sedentarismo (se pratica atividade física menos de três vezes por semana, 30 minutos por sessão), demanda funcional (quantas quadras caminha semanalmente), tempo do início dos sintomas (em anos) e lateralidade ou bilateralidade. Os dados foram correlacionados com a classificação de Ahlbäck aplicada às radiografias realizadas no momento da consulta. Resultados: Uma amostra de 108 pacientes foi estudada. Não encontramos associação entre a classificação de Ahlbäck e a idade do paciente, tabagismo, sedentarismo, lateralidade, quantidade de quadras percorridas por semana, diabetes mellitus e sexo do paciente, porém verificou-se associação positiva em pacientes hipertensos e correlação fraca com altura e peso do paciente e correlação moderada com IMC. Conclusão: A classificação de Ahlbäck não apresenta relação com a maioria dos fatores de risco de gonartrose primária. Nível de evidência III, Estudo caso-controle.

Accuracy of a Radiological Evaluation Method for Thoracic and Lumbar Spinal Curvatures Using Spinous Processes.

OBJECTIVE: The purpose of this study was to assess a radiographic method for spinal curvature evaluation in children, based on spinous processes, and identify its normality limits. METHODS: The sample consisted of 90 radiographic examinations of the spines of children in the sagittal plane. Thoracic and lumbar curvatures were evaluated using angular (apex angle [AA]) and linear (sagittal arrow [SA]) measurements based on the spinous processes. The same curvatures were also evaluated using the Cobb angle (CA) method, which is considered the gold standard. For concurrent validity (AA vs CA), Pearson's product-moment correlation coefficient, root-mean-square error, Pitman- Morgan test, and Bland-Altman analysis were used. For reproducibility (AA, SA, and CA), the intraclass correlation coefficient, standard error of measurement, and minimal detectable change measurements were used. RESULTS: A significant correlation was found between CA and AA measurements, as was a low root-mean-square error. The mean difference between the measurements was 0° for thoracic and lumbar curvatures, and the mean standard deviations of the differences were ±5.9° and 6.9°, respectively. The intraclass correlation coefficients of AA and SA were similar to or higher than the gold standard (CA). The standard error of measurement and minimal detectable change of the AA were always lower than the CA. CONCLUSION: This study determined the concurrent validity, as well as intra- and interrater reproducibility, of the radiographic measurements of kyphosis and lordosis in children.