Phonological properties of logographic words modulate brain activation in bilinguals: a comparative study of Chinese characters and Japanese Kanji.

The brain networks for the first (L1) and second (L2) languages are dynamically formed in the bilingual brain. This study delves into the neural mechanisms associated with logographic-logographic bilingualism, where both languages employ visually complex and conceptually rich logographic scripts. Using functional Magnetic Resonance Imaging, we examined the brain activity of Chinese-Japanese bilinguals and Japanese-Chinese bilinguals as they engaged in rhyming tasks with Chinese characters and Japanese Kanji. Results showed that Japanese-Chinese bilinguals processed both languages using common brain areas, demonstrating an assimilation pattern, whereas Chinese-Japanese bilinguals recruited additional neural regions in the left lateral prefrontal cortex for processing Japanese Kanji, reflecting their accommodation to the higher phonological complexity of L2. In addition, Japanese speakers relied more on the phonological processing route, while Chinese speakers favored visual form analysis for both languages, indicating differing neural strategy preferences between the 2 bilingual groups. Moreover, multivariate pattern analysis demonstrated that, despite the considerable neural overlap, each bilingual group formed distinguishable neural representations for each language. These findings highlight the brain's capacity for neural adaptability and specificity when processing complex logographic languages, enriching our understanding of the neural underpinnings supporting bilingual language processing.

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

BACKGROUND: To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). METHODS: This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. RESULTS: PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. CONCLUSIONS: PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18 F-fluorodeoxy glucose positron emission tomography/MR images.

Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB-IIB cervical cancer using features from 18 F-fluorodeoxy glucose (18 F-FDG) positron emission tomography (PET)/MR images. In this retrospective study, 66 patients with International Federation of Gynecology and Obstetrics stage IB-IIB cervical cancer (22 with PMI and 44 without PMI) who underwent 18 F-FDG PET/MRI were divided into a training dataset (n = 46) and a testing dataset (n = 20). Features were extracted from both the tumoral and peritumoral regions in 18 F-FDG PET/MR images. Single-modality and multimodality radiomics models were developed with random forest to predict PMI. The performance of the models was evaluated with F1 score, accuracy, and area under the curve (AUC). The Kappa test was used to observe the differences between PMI evaluated by radiomics-based models and pathological results. The intraclass correlation coefficient for features extracted from each region of interest (ROI) was measured. Three-fold crossvalidation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from the tumoral region in T2 -weighted images (F1 score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, p = 0.329) and the peritumoral region in PET images (F1 score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, p = 0.202) achieved the best performances in the testing dataset among the four single-ROI radiomics models. The combined model using features from the tumoral region in T2 -weighted images and the peritumoral region in PET images achieved the best performance (F1 score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, p < 0.05). The results suggest that 18 F-FDG PET/MRI can provide complementary information regarding cervical cancer. The radiomics-based method integrating features from the tumoral and peritumoral regions in 18 F-FDG PET/MR images gave a superior performance for evaluating PMI.

Mild photothermal/radiation therapy potentiates ferroptosis effect for ablation of breast cancer via MRI/PA imaging guided all-in-one strategy.

BACKGROUND: Nanotheranostics advances anticancer management by providing therapeutic and diagnostic functions, that combine programmed cell death (PCD) initiation and imaging-guided treatment, thus increasing the efficacy of tumor ablation and efficiently fighting against cancer. However, mild photothermal/radiation therapy with imaging-guided precise mediating PCD in solid tumors, involving processes related to apoptosis and ferroptosis, enhanced the effect of breast cancer inhibition is not fully understood. RESULTS: Herein, targeted peptide conjugated gold nano cages, iRGD-PEG/AuNCs@FePt NPs ternary metallic nanoparticles (Au@FePt NPs) were designed to achieve photoacoustic imaging (PAI)/Magnetic resonance imaging (MRI) guided synergistic therapy. Tumor-targeting Au@FePt forms reactive oxygen species (ROS), initiated by X-ray-induced dynamic therapy (XDT) in collaboration with photothermal therapy (PTT), inducing ferroptosis-augmented apoptosis to realize effective antitumor therapeutics. The relatively high photothermal conversion ability of Au@FePt increases the temperature in the tumor region and hastens Fenton-like processes to achieve enhanced synergistic therapy. Especially, RNA sequencing found Au@FePt inducting the apoptosis pathway in the transcriptome profile. CONCLUSION: Au@FePt combined XDT/PTT therapy activate apoptosis and ferroptosis related proteins in tumors to achieve breast cancer ablation in vitro and in vivo. PAI/MRI images demonstrated Au@FePt has real-time guidance for monitoring synergistic anti-cancer therapy effect. Therefore, we have provided a multifunctional nanotheranostics modality for tumor inhibition and cancer management with high efficacy and limited side effects.

Diagnostic value of integrated 18F-FDG PET/MRI for staging of endometrial carcinoma: comparison with PET/CT.

PURPOSE: To explore the diagnostic value of integrated positron emission tomography/magnetic resonance imaging (PET/MRI) for the staging of endometrial carcinoma and to investigate the associations between quantitative parameters derived from PET/MRI and clinicopathological characteristics of endometrial carcinoma. METHODS: Altogether, 57 patients with endometrial carcinoma who underwent PET/MRI and PET/computed tomography (PET/CT) preoperatively were included. Diagnostic performance of PET/MRI and PET/CT for staging was compared by three readers. Associations between PET/MRI quantitative parameters of primary tumor lesions and clinicopathological characteristics of endometrial carcinoma were analyzed. Histopathological results were used as the standard. RESULTS: The overall accuracy of the International Federation of Gynecology and Obstetrics (FIGO) staging for PET/MRI and PET/CT was 86.0% and 77.2%, respectively. PET/MRI had higher accuracy in diagnosing myometrial invasion and cervical invasion and an equivalent accuracy in diagnosing pelvic lymph node metastasis against PET/CT, although without significance. All PET/MRI quantitative parameters were significantly different between stage I and stage III tumors. Only SUVmax/ADCmin were significantly different between stage I and II tumors. No parameters were significantly different between stage II and III tumors. The SUVmax/ADCmin in the receiving operating characteristic (ROC) curve had a higher area under the ROC curve for differentiating stage I tumors and other stages of endometrial carcinoma. CONCLUSIONS: PET/MRI had a higher accuracy for the staging of endometrial carcinoma, mainly for FIGO stage I tumors compared to PET/CT. PET/MRI quantitative parameters, especially SUVmax/ADCmin, were associated with tumor stage and other clinicopathological characteristics. Hence, PET/MRI may be a valuable imaging diagnostic tool for preoperative staging of endometrial carcinoma.

Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study.

Growing evidence indicates that amyloid-beta (Aß) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aß positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aß PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aß, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aß PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.

Value of integrated PET-IVIM MRI in predicting lymphovascular space invasion in cervical cancer without lymphatic metastasis.

PURPOSE: To evaluate the contributory value of positron emission tomography (PET)-intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in the prediction of lymphovascular space invasion (LVSI) in patients with cervical cancer without lymphatic metastasis. MATERIALS AND METHODS: A total of 90 patients with cervical cancer without signs of lymph node metastasis on PET/MRI were enrolled in this study. The tumours were classified into LVSI-positive (n = 25) and LVSI-negative (n = 65) groups according to postoperative pathology. The PET-derived parameters (SUVmax, SUVmean, metabolic tumour volume (MTV) and total lesion glycolysis (TLG)) and IVIM-derived parameters (ADCmean, ADCmin, Dmean, Dmin, f, D* and gross tumour volume (GTV)) between the two groups were evaluated using a Student's t test (Mann-Whitney U test for variables with a nonnormal distribution) and receiver operating characteristic (ROC) curves. The optimal combination of PET/MR parameters for predicting LVSI was investigated using univariate and multivariate logistic regression models and evaluated by ROC curves. The optimal cutoff threshold values corresponded to the maximal values of the Youden index. A control model was established using 1000 bootstrapped samples, for which the performance was validated using calibration curves and ROC curves. RESULTS: PET-derived parameters (SUVmax, SUVmean, MTV, TLG) and IVIM MRI-derived parameters (Dmin, ADCmin, GTV) were significantly different between patients with and without LVSI (P < 0.05). Logistic analyses showed that a combination of TLG and Dmin had the strongest predictive value for LVSI diagnosis (area under the curve (AUC), 0.861; sensitivity, 80.00; specificity, 86.15; P < 0.001). The optimal cutoff threshold values for Dmin and TLG were 0.58 × 10-3 mm2/s and 66.68 g/cm3, respectively. The verification model showed the combination of TLG and Dmin had the strongest predictive value, and its ROC curve and calibration curve showed good accuracy (AUC, 0.878) and consistency. CONCLUSIONS: The combination of TLG and Dmin may be the best indicator for predicting LVSI in cervical cancer without lymphatic metastasis.

Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma.

BACKGROUND: Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. This study aimed to establish a machine learning model that combines radiomics based on PET imaging with tenascin-C (TNC) and cyclooxygenase-2 (COX-2) for predicting lymphovascular space invasion (LVSI) in patients with early-stage cervical cancer. METHODS: One hundred and twelve patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. Four hundred one radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test. RESULTS: The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models in training and external dataset (AUCs: 0.914, 0.806, respectively, p < 0.001). However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively). CONCLUSION: The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer. In the future, multicentric studies on larger sample of patients will be used to test the model. TRIAL REGISTRATION: This is a retrospective study and there is no experimental intervention on human participants. The Ethics Committee has confirmed that retrospectively registered is not required.

PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer.

OBJECTIVES: To explore the role of radiomics in integrating primary tumor and peritumoral areas based on PET-CT scans for predicting E-cadherin (E-cad) expression in early-stage cervical cancer (ESCC) and its correlation with pelvic lymph node metastasis (PLNM). METHODS: Ninety-seven ESCC patients who had undergone PET-CT scans were retrospectively analyzed. The ROI of primary tumors, peritumoral areas, and plus tumors were semi-automatically segmented on PET-CT images. A total of 1188 radiomics features were extracted, selected, and eventually integrated into radiomics score (rad-score). The rad-score difference between patients with E-cad expression of high and low was analyzed using Mann-Whitney tests. Characteristic correlation was tested using a Spearman analysis. Four models were established using logistic regression algorithms and evaluated using ROC and calibration curves. A DeLong test was used to perform pairwise comparisons of AUCs. RESULTS: The rad-score of patients with low E-cad expression was higher than that of patients with high E-cad expression in both training and testing cohorts (p < 0.001 and p = 0.027, respectively). A significant correlation was observed between the rad-score and E-cad (p < 0.001). PLNM correlated slightly with rad-score and E-cad values (p = 0.01 and p < 0.001, respectively). The ROC curve and calibration curve of the rad-score model performed best in both training and testing cohorts (AUC = 0.915, 0.844, p < 0.001, respectively). CONCLUSIONS: The radiomics of integrating primary tumor and peritumoral areas based on PET-CT showed correlations with PLNM. It was also able to predict E-cad expression in ESCC patients, allowing for evaluation of those patients' prognosis and more individualized medical treatment. KEY POINTS: • By integrating the primary tumor and peritumoral area based on PET-CT, radiomics was feasible. • The rad-score was associated with E-cad expression and PLNM in patients with ESCC. • Radiomics that integrated the primary tumor and peritumoral areas based on PET-CT could predict E-cad expression in patients with ESCC.

Improved Stability and Photothermal Performance of Polydopamine-Modified Fe3 O4 Nanocomposites for Highly Efficient Magnetic Resonance Imaging-Guided Photothermal Therapy.

Magnetic nanomaterials are a promising class of contrast agents for magnetic resonance imaging (MRI). However, their poor stability and low relaxivity are major challenges hindering their clinical applications. In this study, magnetic theranostic nanoagents based on polydopamine-modified Fe3 O4 (Fe3 O4 @PDA) nanocomposites are fabricated for MRI-guided photothermal therapy (PTT) cancer treatments. Their high transverse relaxivity of 337.8 mM-1 s-1 makes these Fe3 O4 @PDA nanocomposites a promising T2 -weighted MRI contrast agent for cancer diagnosis and image-guided cancer therapy. Due to the good photothermal effect of polydopamine (PDA), the tumors of 4T1 tumor-bearing mice are completely excised by PTT. Most importantly, the PDA shell also improves the stability of the Fe3 O4 @PDA nanocomposites, which contributes to their excellent, long-term performance in MRI and PTT applications. Their good stability, high T2 relaxivity, robust biocompatibility, and satisfactory treatment effect give these Fe3 O4 @PDA nanocomposites great potential for use in cancer theranostics.

Value of integrated PET-IVIM MR in assessing metastases in hypermetabolic pelvic lymph nodes in cervical cancer: a multi-parameter study.

PURPOSE: To evaluate the value of integrated multi-parameter positron emission tomography-intravoxel incoherent motion magnetic resonance (PET-IVIM MR) imaging for pelvic lymph nodes with high FDG uptake in cervical cancer, and to determine the best combination of parameters. METHODS: A total of 38 patients with 59 lymph nodes with high FDG uptake were included. The imaging parameters of the lymph nodes were calculated by PET-IVIM MR, and the differences between lymph nodes diagnosed by postoperative pathology as metastasis versus non-metastasis were compared. We used the receiver operating characteristic (ROC) curve and logistic regression to construct a combination prediction model to filter low value and similar parameters, in order to search the optimal combination of PET/MR parameters for predicting pathologically confirmed metastatic lymph nodes. The correlation between diffusion parameters and metabolic parameters was analyzed by Spearman's rank correlation. RESULTS: The maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), total metabolic tumor volume (MTV), total lesion glycolysis (TLG), apparent diffusion coefficient (ADC), diffusion-related coefficient (D), and perfusion-related parameter (F) showed significant differences between the metastatic and non-metastatic groups (p < 0.05). The combination of MTV, SUVmax, and D had the strongest predictive value (area under the ROC 0.983, p < 0.05). SUVmax, SUVmean, and TLG weakly correlated with F (R = - 0.306, - 0.290, and - 0.310; p < 0.05). CONCLUSIONS: The combination of MTV, SUVmax, and D may have a better diagnostic performance than PET- or IVIM-derived parameters either in combination or individually. No strong correlation exists between diffusion parameters and metabolic parameters. KEY POINTS: • Integrated PET-IVIM MR may assist to characterize lymph node status. • The combination of MTV, SUVmax, and D may have a better diagnostic performance than PET- or IVIM-derived parameters either in combination or individually for the assessment of pelvic lymph nodes with high FDG uptake. • No strong correlation exists between diffusion parameters and metabolic parameters in pelvic lymph nodes with high FDG uptake.

Multiparametric PET/MR (PET and MR-IVIM) for the evaluation of early treatment response and prediction of tumor recurrence in patients with locally advanced cervical cancer.

OBJECTIVES: To assess the value of 18F-FDG PET and MR-IVIM parameters before and during concurrent chemoradiotherapy (CCRT) for evaluating early treatment response and predicting tumor recurrence in patients with locally advanced cervical cancer (LACC) using a hybrid PET/MR scanner. METHODS: Fifty-one patients with LACC underwent pelvic PET/MR scans with an IVIM sequence at two time-points (pretreatment [pre] and midtreatment [mid]). Pre- and mid-PET parameters (SUVmax, MTV, TLG) and IVIM parameters (D, F, D*) and their percentage changes (Δ%SUVmax, Δ%MTV, Δ%TLG, Δ%D, Δ%F, Δ%D*) were calculated. We selected independent imaging parameters and built a combined prediction model incorporating imaging parameters and clinicopathological risk factors. The performance of the combinative evaluation for tumor early shrinkage rates (TESR) and the prediction model for tumor recurrence was assessed. RESULTS: Thirty-two patients were classified into the good response (GR) group with TESR ≥ 50%, and 19 patients were categorized into the poor response (PR) group with TESR < 50%. Δ%D (p = 0.013) and Δ%F (p = 0.006) are independently related to TESR with superior combined diagnostic ability (AUC = 0.901). Pre-TLG, Δ%D, and suspicious lymph node metastasis (SLNM) were selected for the construction of the combined prediction model. The model for identifying the patients with high risk of tumor recurrence reached a moderate predictive ability and good stability with c-index of 0.764 (95% CI, 0.672-0.855). CONCLUSION: The combined prediction model based on pretreatment PET metabolic parameter (pre-TLG), IVIM-D percentage changes, and LNs status provides great potential to identify the LACC patients with high risk of recurrence at early stage of CCRT. KEY POINTS: • PET/MR plus IVIM offers various complementary information for LACC. • IVIM-D and IVIM-F percentage changes are independently related to tumor early shrinkage rates. • The combined prediction model can help identify the LACC patients with high risk of tumor recurrence.

18F-FDG PET/CT Quantitative Parameters and Texture Analysis Effectively Differentiate Endometrial Precancerous Lesion and Early-Stage Carcinoma.

OBJECTIVE: This study evaluated the metabolic parameters and texture features of fluorodeoxyglucose positron emission tomography-computed tomography (PET/CT) for the diagnosis and differentiation of endometrial atypical hyperplasia (EAH), EAH with field cancerization (FC), and stage 1A endometrial carcinoma (EC 1a). MATERIALS AND METHODS: We retrospectively analyzed the metabolic parameters of PET/CT in 170 patients with diagnoses confirmed by pathology, including 57 cases of EAH (57/170, 33.53%), 45 cases of FC (45/170, 26.47%), and 68 cases of EC 1a (68/170, 40.0%). Then, the texture features of each tumor were extracted and compared with the metabolic parameters and pathological results using nonparametric tests and linear regression analysis. The diagnostic performance was assessed by the area under the curve (AUC) values obtained from receiver operating characteristic analysis. RESULTS: There were moderate positive correlations between the PET standardized uptake values (SUVpeak, SUVmax, and SUVmean) and postoperative pathological features with correlation coefficients (rs) of 0.663, 0.651, and 0.651, respectively (P < .001). Total lesion glycolysis showed relatively low correlation with pathological characteristics (rs = 0.476), whereas metabolic tumor volume and age showed the weakest correlations (rs = 0.186 and 0.232, respectively). To differentiate between the diagnosis of EAH and FC, SUVmax displayed the largest AUC of 0.857 (sensitivity, 82.2%; specificity, 84.2%). Five texture features were screened out as Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 (P < .001) by linear model of texture analysis (AUC = 0.851; specificity = 0.692; sensitivity = 0.871). To differentiate between the diagnoses of FC and EC 1a, SUVpeak displayed the largest AUC of 0.715 (sensitivity, 67.6%; specificity, 77.8%), and 2 texture features were identified as Percentile 10 and CP_angle 135_offset 7 (AUC = 0.819; specificity = 0.871; sensitivity = 0.766; P < .001). CONCLUSIONS: SUVmax and SUVpeak had the highest diagnostic values for EAH, FC, and EC 1a compared with the other tested parameters. SUVmax, Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 distinguished EAH from FC. SUVpeak, Percentile 10, and ClusterProminence_angle 135_offset 7 distinguished FC from EC 1a. This study showed that the addition of texture features provides valuable information for differentiating EAH, FC, and EC 1a diagnoses.

Amide proton transfer imaging to evaluate the grading of squamous cell carcinoma of the cervix: A comparative study using 18 F FDG PET.

BACKGROUND: Amide proton transfer (APT) imaging has shown great potential value in the diagnosis of cancer, but has yet not been applied in cervical carcinoma patients. PURPOSE: To investigate the utility of APT imaging in estimating histologic grades of squamous cell carcinoma of the cervix (SCCC), compared with the standardized uptake value (SUV). STUDY TYPE: Prospective. POPULATION: Thirty-one patients with SCCC (median age 51 years) were included. FIELD STRENGTH/SEQUENCE: Ingenia 3.0 T CX, Axial T1 -weighted imaging (T1 WI), Axial T2 WI, 3D turbo spin echo sequence for APT imaging. ASSESSMENT: Patient pathology was confirmed by surgery and the patients were divided into three groups based on histologic grades: Grade 1 (n = 9), Grade 2 (n = 12), and Grade 3 (n = 10). The APT signal intensity (APT SI), maximum SUV (SUVmax ) and mean SUV (SUVmean ) for each grade were assessed by experienced radiologists in a blinded manner. STATISTICAL TESTS: The obtained parameters were compared by one-way analysis of variance with Tukey honest significant difference post-hoc test. The correlations between the parameters and histologic grades were analyzed by Spearman's correlation coefficient. The Pearson correlation coefficients of the APT SI with the SUVmax and SUVmean were also calculated. RESULTS: The APT SIs for the three grades were significantly different (P = 0.0002). The APT SIs of Grade 2 and Grade 3 had significant differences (P = 0.009). The Spearman correlation coefficients for the correlations between the parameters and histological grade were as follows: APT SI: 0.684 (P = 0.00002), SUVmax : 0.318 (P = 0.082), and SUVmean : 0.261 (P = 0.157). The Pearson correlation coefficients of the APT SI with the SUVmax and SUVmean were 0.108 (P = 0.564) and 0.178 (P = 0.337), respectively. DATA CONCLUSION: The APT SI was positively correlated with the SCCC grades. APT imaging maybe a promising method for predicting SCCC histologic grades. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:261-268.

Automated detection of the arterial input function using normalized cut clustering to determine cerebral perfusion by dynamic susceptibility contrast-magnetic resonance imaging.

PURPOSE: To propose a new clustering method for the automatic detection of arterial input function (AIF) with high accuracy in dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI). MATERIALS AND METHODS: A novel method for automatically determining the AIF was proposed to facilitate the analysis of MR perfusion, which relied on normalized cut (Ncut) clustering. Its performance was compared with those of two other previously reported clustering methods: k-means and fuzzy c-means (FCM) techniques, in terms of the detection accuracy and computational time. Both simulated perfusion data and data collected from 42 healthy human subjects were applied to investigate the feasibility of the proposed approach. RESULTS: In the simulation study, the partial volume effect (PVE) level, peak value (PV), time to peak (TTP), full width at half maximum (FWHM), area under AIF curve (AUC), root mean square error (RMSE) between the estimated AIF and true AIF, and M value given by [PV/(FWHM×TTP)] were 45.45, 4.2737, 29.92, 6.4563, 76.4836, 0.0519, and 0.0221 for Ncut-based AIF, 96.45, 3.8385, 31.74, 7.5133, 75.7364, 0.3295, and 0.0161 for FCM-based AIF, 91.18, 3.8990, 31.73, 7.4544, 76.0476, 0.3128, and 0.0165 for k-means-based AIF, 0, 4.4592, 29.51, 6.2016, 76.8669, 0, and 0.0244 for true AIF. In the clinical study, the mean PV, TTP, FWHM, AUC, M, error between estimated AIF and manual AIF were 1.7395, 30.95, 5.5923, 19.1081, 0.0397, and 0.4406 for Ncut-based AIF, 1.3629, 31.31, 6.8616, 17.9992, 0.0123, and 0.0846 for k-means-based AIF, 1.2101, 31.61, 7.1729, 16.6238, 0.0102, and 0.1016 for FCM-based AIF. The differences in PV, M, FWHM, and error reached a significant level (P = 0.032, 0.010, 0.003, and 0.002, respectively) between Ncut and k-means methods as well as between Ncut and FCM methods (P = 0.013, 0.008, 0.007, and 0.009, respectively). There was no significant difference in TTP between Ncut and each of the other two methods (P = 0.173 and 0.097, respectively). For AUC, a significant difference was found between Ncut and FCM algorithms (P = 0.025), but not between Ncut and k-means methods (P = 0.138). The mean execution time was 0.4406 for the Ncut method, 0.2649 for the k-means method, and 0.1371 for the FCM method, and the differences were significant both between Ncut and k-means methods (P = 0.002) and between Ncut and FCM methods (P = 0.004). CONCLUSION: Ncut clustering yield AIFs more in line with the expected AIF, and might be preferred to FCM and k-means clustering methods sensitive to randomly selected initial centers.

Anatomical and functional volume concordance between FDG PET, and T2 and diffusion-weighted MRI for cervical cancer: a hybrid PET/MR study.

PURPOSE: To evaluate the concordance among (18)F-FDG PET imaging, MR T2-weighted (T2-W) imaging and apparent diffusion coefficient (ADC) maps with diffusion-weighted (DW) imaging in cervical cancer using hybrid whole-body PET/MR. METHODS: This study prospectively included 35 patients with cervical cancer who underwent pretreatment (18)F-FDG PET/MR imaging. (18)F-FDG PET and MR images were fused using standard software. The percent of the maximum standardized uptake values (SUV max) was used to contour tumours on PET images, and volumes were calculated automatically. Tumour volumes measured on T2-W and DW images were calculated with standard techniques of tumour area multiplied by the slice profile. Parametric statistics were used for data analysis. RESULTS: FDG PET tumour volumes calculated using SUV max (14.30 ± 4.70) and T2-W imaging volume (33.81 ± 27.32 cm(3)) were similar (P > 0.05) at 35 % and 40 % of SUV max (32.91 ± 18.90 cm(3) and 27.56 ± 17.19 cm(3) respectively) and significantly correlated (P < 0.001; r = 0.735 and 0.766). The mean DW volume was 30.48 ± 22.41 cm(3). DW volumes were not significantly different from FDG PET volumes at either 35 % SUV max or 40 % SUV max or from T2-W imaging volumes (P > 0.05). PET subvolumes with increasing SUV max cut-off percentage showed an inverse change in mean ADC values on DW imaging (P < 0.001, ANOVA). CONCLUSION: Hybrid PET/MR showed strong volume concordance between FDG PET, and T2-W and DW imaging in cervical cancer. Cut-off at 35 % or 40 % of SUV max is recommended for (18)F-FDG PET/MR SUV-based tumour volume estimation. The linear tumour subvolume concordance between FDG PET and DW imaging demonstrates individual regional concordance of metabolic activity and cell density.

Comparison of tumor volume between PET and MRI in cervical cancer with hybrid PET/MR.

OBJECTIVE: This study aimed to compare the tumor volume between magnetic resonance imaging-defined gross tumor volume (MR-GTV) and positron emission tomography-defined GTV (PET-GTV) in cervical cancer with hybrid PET/MR. MATERIALS AND METHODS: Twenty-seven patients with cervical cancer underwent PET/MR pelvic examination before radiotherapy. The MR-GTV was manually outlined on T2-weighted MR images. The PET-GTV was autocontoured on PET images using a 40% maximum standardized uptake value threshold. Results were analyzed by Pearson analysis, Bland-Altman plot, and 1-way analysis of variance. RESULTS: Magnetic resonance imaging-GTV significantly correlated with PET-GTV (r(2) = 0.797, P < 0.001). The Bland-Altman plot showed a bad agreement between MR-GTV and PET-GTV. The PET-GTV underestimated the MR-GTV in 23 of 27 tumors. Patients were divided into the following 3 groups according to MR-GTV: less than 14 mL (n = 6), 14 to 62 mL (n = 12), and 62 mL or more (n = 9). The mean (SD) MR-GTV, PET-GTV, ratio, and overlap between MR-GTV and PET-GTV for the less than 14 mL cohort were 9.6 (2.6) mL, 16.7 (10.1) mL, 0.77 (0.40), and 0.47 (0.20), respectively. The PET-GTV overestimated MR-GTV in 4 of the 6 lesions by a mean (SD) of 11.1 (9.4) mL. Among the 14 to 62 mL cohort, the mean (SD) MR-GTV, PET-GTV, ratio, and overlap were 38.6 (14.5) mL, 24.9 (8.6 mL), 1.54 (0.25), and 0.87 (0.08), respectively. The PET-GTV underestimated MR-GTV for 12 tumors by a mean (SD) of 13.7 (8.4) mL. In the 62 mL or more cohort, the mean (SD) MR-GTV, PET-GTV, ratio, and overlap were 85.9 (25.8) mL, 54.3 (14.1) mL, 1.61 (0.35), and 0.87 (0.09), respectively. The PET-GTV underestimated MR-GTV 9 tumors by a mean (SD) of 31.6 (19.5) mL. The ratio and overlap differences were statistically significant among groups (F = 14.619, P < 0.001; F = 25.134, P < 0.001). CONCLUSIONS: Tumor volume discrepancies were observed between MR-GTV and PET-GTV for cervical cancer. With an increasing tumor volume, there was an increase in the difference between MR-GTV and PET-GTV. In addition, larger tumors had a higher degree of overlap compared with small tumors.

CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation.

A benchmark histopathological Hematoxylin and Eosin (H&E) image dataset for Cervical Adenocarcinoma in Situ (CAISHI), containing 2240 histopathological images of Cervical Adenocarcinoma in Situ (AIS), is established to fill the current data gap, of which 1010 are images of normal cervical glands and another 1230 are images of cervical AIS. The sampling method is endoscope biopsy. Pathological sections are obtained by H&E staining from Shengjing Hospital, China Medical University. These images have a magnification of 100 and are captured by the Axio Scope. A1 microscope. The size of the image is 3840 × 2160 pixels, and the format is ".png". The collection of CAISHI is subject to an ethical review by China Medical University with approval number 2022PS841K. These images are analyzed at multiple levels, including classification tasks and image retrieval tasks. A variety of computer vision and machine learning methods are used to evaluate the performance of the data. For classification tasks, a variety of classical machine learning classifiers such as k-means, support vector machines (SVM), and random forests (RF), as well as convolutional neural network classifiers such as Residual Network 50 (ResNet50), Vision Transformer (ViT), Inception version 3 (Inception-V3), and Visual Geometry Group Network 16 (VGG-16), are used. In addition, the Siamese network is used to evaluate few-shot learning tasks. In terms of image retrieval functions, color features, texture features, and deep learning features are extracted, and their performances are tested. CAISHI can help with the early diagnosis and screening of cervical cancer. Researchers can use this dataset to develop new computer-aided diagnostic tools that could improve the accuracy and efficiency of cervical cancer screening and advance the development of automated diagnostic algorithms.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

BACKGROUND: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. METHODS: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. RESULTS: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. CONCLUSION: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage.

BACKGROUND AND OBJECTIVE: Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. METHODS: This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. RESULTS: This study conducts numerous experiments using classical machine learning and deep learning methods, demonstrating the differences in various segmentation and detection methods on the PHE-SICH-CT-IDS. The highest precision achieved in semantic segmentation is 76.31%, while object detection attains a maximum precision of 97.62%. The experimental results on radiomic feature extraction and analysis prove the suitability of PHE-SICH-CT-IDS for evaluating image features and highlight the predictive value of these features for the prognosis of SICH patients. CONCLUSION: To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.