Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features.

OBJECTIVE: This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). METHODS: Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. RESULTS: From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. CONCLUSION: This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.

A radiomics model based on magnetic resonance imaging to predict cytokeratin 7/19 expression and liver fluke infection of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. HCC with liver fluke infection could harbor unique biological behaviors. This study was aimed at investigating radiomics features of HCC with liver fluke infection and establishing a model to predict the expression of cytokeratin 7 (CK7) and cytokeratin 19 (CK19) as well as prognosis at the same time. A total of 134 HCC patients were included. Gadoxetic acid-enhanced magnetic resonance imaging (MRI) images of all patients were acquired. Radiomics features of the tumor were extracted and then data dimensionality was reduced. The radiomics model was established to predict liver fluke infection and the radiomics score (Radscore) was calculated. There were 11 features in the four-phase combined model. The efficiency of the combined model increased significantly compared to each single-phase MRI model. Radscore was an independent predictor of liver fluke infection. It was also significantly different between different expression of CK7/ CK19. Meanwhile, liver fluke infection was associated with CK7/CK19 expression. A cut-off value was set up and all patients were divided into high risk and low risk groups of CK7/CK19 positive expression. Radscore was also an independent predictor of these two biomarkers. Overall survival (OS) and recurrence free survival (RFS) of negative liver fluke infection group were significantly better than the positive group. OS and RFS of negative CK7 and CK19 expression were also better, though not significantly. Positive liver fluke infection and CK19 expression prediction groups harbored significantly worse OS and RFS, survival of positive CK7 expression prediction was unsatisfying as well. A radiomics model was established to predict liver fluke infection among HCC patients. This model could also predict CK7 and CK19 expression. OS and RFS could be foreseen by this model at the same time.

Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) is an independent prognostic factor for poor progression-free and overall survival. Radiomics can help extract imaging information associated with tumor pathophysiology. AIM: To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in sHCC. METHODS: In total, 415 patients were diagnosed with sHCC by postoperative pathology. A total of 221 patients were retrospectively included from our hospital. In addition, we recruited 94 and 100 participants as independent external validation sets from two other hospitals. Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging (DWI) were constructed and validated using machine learning. As presented in the radiomics nomogram, a prediction model was developed using multivariable logistic regression analysis, which included radiomics scores, radiologic features, and clinical features, such as the alpha-fetoprotein (AFP) level. The calibration, decision-making curve, and clinical usefulness of the radiomics nomogram were analyzed. The radiomic nomogram was validated using independent external cohort data. The areas under the receiver operating curve (AUC) were used to assess the predictive capability. RESULTS: Pathological examination confirmed MVI in 64 (28.9%), 22 (23.4%), and 16 (16.0%) of the 221, 94, and 100 patients, respectively. AFP, tumor size, non-smooth tumor margin, incomplete capsule, and peritumoral hypointensity in hepatobiliary phase (HBP) images had poor diagnostic value for MVI of sHCC. Quantitative radiomic features (1409) of MRI scans) were extracted. The classifier of logistic regression (LR) was the best machine learning method, and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set (hospital A) and validation set (hospital B, C). The AUC of HBP was 0.979, 0.970, and 0.803, respectively, and the AUC of DWI was 0.971, 0.816, and 0.801 (P < 0.05), respectively. Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts (C-index of HBP and DWI were 0.971, 0.912, 0.808, and 0.970, 0.843, 0.869, respectively). The clinical usefulness of the nomogram was further confirmed using decision curve analysis. CONCLUSION: AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC. Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI. The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.

Comparison of 10- and 20-min hepatobiliary phase images on Gd-EOB-DTPA-enhanced MRI T1 mapping for liver function assessment in clinic.

PURPOSE: To compare hepatobiliary phase (HBP) images obtained 10 and 20 min after Gd-EOB-DTPA-enhanced MRI for liver function assessment in clinic on 3.0 T MR imaging. METHODS: 103 patients were separated into four groups: 38 patients for the normal liver function (NLF) group, 33 patients for the liver cirrhosis with Child-Pugh A (LCA) group, 21 patients for the liver cirrhosis with Child-Pugh B group, and 11 patients for a liver cirrhosis with Child-Pugh C group. T1 relaxation times (T1rt) were measured on T1 mapping and reduction rates of T1rt (rrT1rt) were calculated. HBP images were obtained at the 10- and 20-min mark after Gd-EOB-DTPA enhancement. RESULTS: T1rt on pre-enhancement imaging showed no significant difference (p > 0.05) among all four groups. T1rt for both the 10-min HBP and the 20-min HBP showed a significant difference (p < 0.05) among all groups, but showed no significant difference (p > 0.05) between the NLF group and the LCA group. T1rt and rrT1rt showed no significant difference (p > 0.05) between 10-min HBP and 20-min HBP among all groups. The ROC analysis on 10-min HBP and 20-min HBP showed a lower diagnostic performance between NLF group and LCA group (AUC from 0.532 to 0.582), but high diagnostic performance (AUC from 0.788 to 1.000) among others group. CONCLUSIONS: In comparing 10-min HBP and 20-min HBP T1 mapping after Gd-EOB-DTPA enhancement, our results suggest that 10-min HBP T1 mapping is a feasible option for quantitatively assessing liver function.

Evaluating segmental liver function using T1 mapping on Gd-EOB-DTPA-enhanced MRI with a 3.0 Tesla.

BACKGROUND: Assessing the liver function provides valuable information to evaluate surgical risk and plan accordingly. Current studies focus on whole liver function evaluation. However, assessment of segmental liver function is equally important in the clinical practice. The purpose of this study was to investigate whether Gd-EOB-DTPA-enhanced MRI can evaluate the liver function of each segment by using T1 mapping at 3 Tesla MRI. METHODS: One hundred three patients were classified into one of 4 groups: a normal liver function (NLF) group (n = 38), a liver cirrhosis with Child-Pugh A (LCA) group (n = 33), a liver cirrhosis with Child-Pugh B (LCB) group (n = 21), and a liver cirrhosis with Child-Pugh C (LCC) group (n = 11). All patients underwent Gd-EOB-DTPA-enhanced MRI scans. T1 relaxation times were measured on the liver superimposing T1 mapping images. Reduction rate (△%) of T1 relaxation time of the liver parenchyma were calculated. RESULTS: After 20 min of Gd-EOB-DTPA enhancement, the T1 relaxation time of all liver segments in the LCC group were different from those in all the other groups, and more liver segments from the LCB and LCA groups different from the NLF group (p < 0.05). For the LCB group, the areas under the receiver operating characteristic curves (AUCs) of different liver segments for hepatobiliary phase (HBP) were 0.654-0.904 on T1 relaxation time, and 0.709-0.905 on △%. For the LCC group, the AUCs of different liver segments for HBP were 0.842-0.997 on T1 relaxation time, and 0.887-0.990 on △%. CONCLUSIONS: For LCB patients, segmental liver function evaluation is possible using Gd-EOB-DTPA-enhanced MRI T1 mapping. For LCC patients, all liver segments can be used to evaluate liver function and both T1 relaxation time and the △% of T1 relaxation time have good diagnostic performance.

Gd-EOB-DTPA-enhanced MRI T1 mapping for assessment of liver function in rabbit fibrosis model: comparison of hepatobiliary phase images obtained at 10 and 20 min.

OBJECTIVES: To compare hepatobiliary phase (HBP) images obtained at 10 and 20 min after Gd-EOB-DTPA-enhanced MRI for assessment of liver function in rabbit fibrosis model on 3.0 T MR imaging. METHODS: 34 animals were separated into three groups: 5 for a control group, 14 for a mild fibrosis group, and 15 for a severe fibrosis group based on pathological proof. T1 relaxation times (T1rt) were measured on T1 mapping and reduction rates of T1rt (rrT1rt) were calculated. HBP images were obtained at 10 and 20 min after Gd-EOB-DTPA enhancement. Indocyanine green retention rates at 15 min (ICG R15) were performed for all animals. RESULTS: T1rt on pre-enhancement imaging showed no significant difference (p > 0.05) among all groups. T1rt on 10 min HBP and 20 min HBP showed significant difference (p < 0.05) among all groups. T1rt and rrT1rt in three groups showed no-significant difference (p > 0.05) between 10 min HBP and 20 min HBP. T1rt on both 10 and 20 min HBP showed significant correlation with ICG R15 (p < 0.05); rrT1rt on both 10 min HBP and 20 min HBP showed significant inverse correlation with ICG R15 (p < 0.05). CONCLUSIONS: Comparing 10 min HBP and 20 min HBP T1 mapping after Gd-EOB-DTPA enhancement, our results suggest that 10 min HBP T1 mapping is feasible for quantitatively assessing liver function.

Conventional MRI techniques combined with MR sialography on T2-3D-DRIVE in Sjögren syndrome.

AIM: To investigate the value of conventional MRI techniques combined with MR sialography on T2-3D-DRIVE in the diagnosis of Sjögren syndrome (SS). METHODS: 107 patients were divided into SS group and non-SS group. Conventional MRI techniques, such as T1WI, T2WI, and STIR images were used for changes of fat signal in the parotid gland, while the MR sialography were used for ducts dilation of the parotid gland. RESULTS: Among 93 SS patients, MRI identified abnormal fat deposit in the parotid glands in 86 patients. The fat signal based on MRI images showed 7 patients were in stage 0, 28 in stage 1, 14 in stage 2, 32 in stage 3 and 12 in stage 4. T2-3D-DRIVEMR MR sialography identified peripheral ducts dilation in 86 patients. The duct dilation based on MR sialography showed 7 patients in stage 0, 14 patients in stage 1, 44 patients in stage 2, 26 patients in stage 3, and 2 patients in stage 4. On MRI and MR sialography, both had a positive diagnostic rate of 92.5%. When MRI and MR sialography techniques were used together, the positive diagnostic rate increased to 96.8%. However, Kappa test showed that the MRI fat signal staging and MR sialogrpahy duct dilation staging had statistical difference (Kappa = 0.241, P = 0.000). CONCLUSION: T2-3D-DRIVE MR sialography detects peripheral ducts dilation in parotid glands with unmatched spatial resolution, also MRI fat suppression techniques detect diffusive fat deposit in parotid glands with high accuracy. Combining two techniques will provide optimal diagnosis workup for SS.

Vulnerability of welders to manganese exposure--a neuroimaging study.

Increased manganese (Mn) exposure is known to cause cognitive, psychiatric and motor deficits. Mn exposure occurs in different occupational settings, where the airborne Mn level and the size of respirable particulates may vary considerably. Recently the importance of the role of the cerebral cortex in Mn toxicity has been highlighted, especially in Mn-induced neuropsychological effects. In this study we used magnetic resonance imaging (MRI) to evaluate brain Mn accumulation using T1 signal intensity indices and to examine changes in brain iron content using T2* contrast, as well as magnetic resonance spectroscopy (MRS) to measure exposure-induced metabolite changes non-invasively in cortical and deep brain regions in Mn-exposed welders, Mn-exposed smelter workers and control factory workers with no measurable exposure to Mn. MRS data as well as T1 signal intensity indices and T2* values were acquired from the frontal cortex, posterior cingulate cortex, hippocampus, and thalamus. Smelters were exposed to higher air Mn levels and had a longer duration of exposure, which was reflected in higher Mn levels in erythrocytes and urine than in welders. Nonetheless, welders had more significant metabolic differences compared to controls than did the smelter workers, especially in the frontal cortex. T1 hyperintensities in the globus pallidus were observed in both Mn-exposed groups, but only welders showed significantly higher thalamic and hippocampal T1 hyperintensities, as well as significantly reduced T2* values in the frontal cortex. Our results indicate that (1) the cerebral cortex, in particular the frontal cortex, is clearly involved in Mn neurotoxic effects and (2) in spite of the lower air Mn levels and shorter duration of exposure, welders exhibit more extensive neuroimaging changes compared to controls than smelters, including measurable deposition of Mn in more brain areas. These results indicate that the type of exposure (particulate sizes, dust versus fume) and route of exposure play an important role in the extent of Mn-induced toxic effects on the brain.

Thalamic GABA predicts fine motor performance in manganese-exposed smelter workers.

Overexposure to manganese (Mn) may lead to parkinsonian symptoms including motor deficits. The main inhibitory neurotransmitter gamma-aminobutyric acid (GABA) is known to play a pivotal role in the regulation and performance of movement. Therefore this study was aimed at testing the hypothesis that an alteration of GABA following Mn exposure may be associated with fine motor performance in occupationally exposed workers and may underlie the mechanism of Mn-induced motor deficits. A cohort of nine Mn-exposed male smelter workers from an Mn-iron alloy factory and 23 gender- and age-matched controls were recruited and underwent neurological exams, magnetic resonance spectroscopy (MRS) measurements, and Purdue pegboard motor testing. Short-echo-time MRS was used to measure N-Acetyl-aspartate (NAA) and myo-inositol (mI). GABA was detected with a MEGA-PRESS J-editing MRS sequence. The mean thalamic GABA level was significantly increased in smelter workers compared to controls (p = 0.009). Multiple linear regression analysis reveals (1) a significant association between the increase in GABA level and the duration of exposure (R(2) = 0.660, p = 0.039), and (2) significant inverse associations between GABA levels and all Purdue pegboard test scores (for summation of all scores R(2) = 0.902, p = 0.001) in the smelter workers. In addition, levels of mI were reduced significantly in the thalamus and PCC of smelter workers compared to controls (p = 0.030 and p = 0.009, respectively). In conclusion, our results show clear associations between thalamic GABA levels and fine motor performance. Thus in Mn-exposed subjects, increased thalamic GABA levels may serve as a biomarker for subtle deficits in motor control and may become valuable for early diagnosis of Mn poisoning.

In vivo measurement of brain GABA concentrations by magnetic resonance spectroscopy in smelters occupationally exposed to manganese.

BACKGROUND: Exposure to excessive levels of manganese (Mn) is known to induce psychiatric and motor disorders, including parkinsonian symptoms. Therefore, finding a reliable means for early detection of Mn neurotoxicity is desirable. OBJECTIVES: Our goal was to determine whether in vivo brain levels of γ-aminobutyric acid (GABA), N-acetylaspartate (NAA), and other brain metabolites in male smelters were altered as a consequence of Mn exposure. METHODS: We used T1-weighted magnetic resonance imaging (MRI) to visualize Mn deposition in the brain. Magnetic resonance spectroscopy (MRS) was used to quantify concentrations of NAA, glutamate, and other brain metabolites in globus pallidus, putamen, thalamus, and frontal cortex from a well-established cohort of 10 male Mn-exposed smelters and 10 male age-matched control subjects. We used the MEGA-PRESS MRS sequence to determine GABA levels in a region encompassing the thalamus and adjacent parts of the basal ganglia [GABA-VOI (volume of interest)]. RESULTS: Seven of 10 exposed subjects showed clear T1-hyperintense signals in the globus pallidus indicating Mn accumulation. We found a significant increase (82%; p = 0.014) in the ratio of GABA to total creatine (GABA/tCr) in the GABA-VOI of Mn-exposed subjects, as well as a distinct decrease (9%; p = 0.04) of NAA/tCr in frontal cortex that strongly correlated with cumulative Mn exposure (R = -0.93; p < 0.001). CONCLUSIONS: We demonstrated elevated GABA levels in the thalamus and adjacent basal ganglia and decreased NAA levels in the frontal cortex, indicating neuronal dysfunction in a brain area not primarily targeted by Mn. Therefore, the noninvasive in vivo MRS measurement of GABA and NAA may prove to be a powerful tool for detecting presymptomatic effects of Mn neurotoxicity.

Relationship between changes in brain MRI and (1)H-MRS, severity of chronic liver damage, and recovery after liver transplantation.

Magnetic resonance imaging (MRI) and (1)H magnetic resonance spectroscopy ((1)H-MRS) have been used in clinics for diagnosis of chronic liver diseases. This study was designed to investigate the relationship between MRI/MRS outcomes and the severity of liver damage. Of 50 patients examined, the MRI signal intensity in the globus pallidus as determined by pallidus index (PI) increased as the disease severity (scored by Child Pugh ranking) worsened (r = 0.353, P < 0.05). The changes in PI values were also linearly associated with Mn concentrations in whole blood (MnB) (r = 0.814, P < 0.01). MRS analysis of four major brain metabolites (i.e., Cho, mI, Glx, and NAA) revealed that the ratios of Cho/Cr and mI/Cr in cirrhosis and CHE patients were significantly decreased in comparison to controls (P < 0.05), whereas the ratio of Glx/Cr was significantly increased (P < 0.05). The Child Pugh scores significantly correlated with mI/Cr (-0.484, P < 0.01) and Glx (0.369, P < 0.05), as well as MnB (0.368, P < 0.05), but not with other brain metabolites. Three patients who received a liver transplant experienced normalization of brain metabolites within 3 months of post-transplantation; the MR imaging of Mn in the globus pallidus completely disappeared 5 months after the surgery. Taken together, this clinical study, which combined MRI/MRS analysis, autopsy exam and liver transplant, clearly demonstrates that liver injury-induced brain Mn accumulation can reversibly alter the homeostasis of brain metabolites Cho, mI and Glx. Our data further suggest that liver transplantation can restore normal brain Mn levels.

[Variations of brain magnetic resonance imaging among manganese-exposed workers].

OBJECTIVE: Variations of the signal intensities in the magnetic resonance (MR) T(1)-weighted image (T(1)WI) of globus pallidus among manganese(Mn)-exposed workers were explored to provide a scientific basis for exposed biomarker of manganese-injured central nervous system (CNS). METHODS: The brain MR T(1) and T(2) WI in eighteen male asymptomatic Mn-exposed, eight manganism and nine healthy control workers were examined routinely by adopting a 1.5 Tesla signal superconducting system. The SIGP and the signal intensity in frontal white matter (SIFWM) in the same side were determined, then pallidal index (PI) was calculated. Concentration of MnO(2) in workplaces and content of manganese in red blood cell (MnRBC) among workers were respectively determined by flame atomic absorption spectrometer (AAS) and inductively coupled plasma-atomic emission spectrophotometry (ICP-AES). The follow-up investigation in the eight high Mn-exposed workers was made one year later. RESULTS: The results showed that the median of air MnO(2) in smelting workplace was 0.64 mg/m(3)(0.07 - 5.40 mg/m(3)), which were respective 0.56 mg/m(3)(0.09 - 1.71 mg/m(3)) in power distribution room (low Mn-exposure) and 0.89 mg/m(3) (0.07 - 5.40 mg/m(3)) in furnace (high Mn-exposure). PI in the Mn-exposed and high Mn-exposed workers were both higher than those of the manganism and control workers(116.4 +/- 8.2, 119.0 +/- 7.9, 105.3 +/- 8.4 and 102.2 +/- 1.5, respectively. Mn vs control, t' = 7.146, P = 0.000; Mn vs manganism, t = 3.181, P = 0.004. High Mn-exposure vs control, t' = 7.446, P = 0.000; high Mn-exposure vs manganism, t = 3.763, P = 0.001). The increased signal in T(1)WI of globus pallidus was observed in Mn-exposed workers, especially in high Mn-exposed workers. The content of manganese in red blood cell of Mn-exposed and control workers was significantly higher than those of the manganism workers [(151.6 +/- 40.5) ng/ml, (149.2 +/- 21.3) ng/ml, (154.5 +/- 46.6) ng/ml, (144.4 +/- 14.2) ng/ml, (20.8 +/- 7.4) ng/ml respectively. The difference was significant in statistics. Manganism vs control, t = 20.206, P = 0.000; manganism vs Mn, t' = 13.144, P = 0.000; manganism vs low and high Mn, t' = 12.964, 9.957, respectively, P = 0.000]. Only a decreased median of air MnO(2) in furnace was found one year later (0.89, 0.31 mg/m(3), Z = -2.142, P = 0.032). The difference was significant in statistics. CONCLUSION: Our data suggests that SIGP of MR T(1)WI among workers was obviously increased by manganese-exposure. PI may be taken as the signal of CNS injury which was induced by manganese-exposure.

Evidence for altered hippocampal volume and brain metabolites in workers occupationally exposed to lead: a study by magnetic resonance imaging and (1)H magnetic resonance spectroscopy.

Environmental and occupational exposure to lead (Pb) remains to be a major public health issue. The purpose of this cross-sectional study was to use non-invasive magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy ((1)H MRS) techniques to investigate whether chronic exposure to Pb in an occupational setting altered brain structure and function of Pb-exposed workers. The Pb-exposed group consisted of 15 workers recruited from either a Pb-smelting factory or a Pb-battery manufacturer. The control group had 19 healthy volunteers who had no history of Pb exposure in working environment or at home. The average airborne Pb concentrations in fume and dust were 0.43 and 0.44 mg/m(3), respectively, in the smeltery, and 0.10 and 1.06 mg/m(3), respectively, in the Pb battery workshop. The average blood Pb concentrations (BPb) in Pb-exposed and control workers were 63.5 and 8.7 microg/dL, respectively. The MRI examination showed that brain hippocampal volume among Pb-exposed workers was significantly diminished in comparison to age-matched control subjects (p < 0.01), although the extent of this reduction was relatively small (5-6% of the control values). Linear regression analyses revealed significant inverse associations between BPb and the decreased hippocampal volume on both sides of brain hemisphere. Among five brain metabolites investigated by MRS, i.e., N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho), inosine (mI), glutamate/glutamine (Glx) and lipids (Lip), a significant decrease in NAA/Cr ratio (7% of controls, p < 0.05) and a remarkable increase in Lip/Cr ratio (40%, p < 0.01) were observed in the brains of Pb-exposed workers as compared to controls. Furthermore, the increased Lip/Cr ratio was significantly associated with BPb (r = 0.46, p < 0.01). Taken together, this study suggests that occupational exposure to Pb may cause subtle structural and functional alteration in human brains. The MRI and MRS brain imaging techniques can be used as the non-invasive means to evaluate Pb-induced neurotoxicity.