Objective:To investigate the uptake patterns of 18F-fluorodeoxy glucose ( 18F-FDG) in the endometrium using positron emission tomography (PET) imaging and to differentiate these from stage IA endometrial cancer. Methods:From September 2022 to April 2024, a prospective inclusion of 354 women without gynecological diseases and no hormone usage who underwent 18F-FDG PET-CT examinations at the affiliated hospital of Qingdao University were set as the physiological group, while a group containing 42 cases of Stage IA endometrial carcinoma was also set. The physiological group was divided into five groups based on the menstrual cycle: menstrual period, proliferative phase, ovulatory phase, secretory phase, and menopausal phase. The images were analyzed using visual and quantitative measurements; quantitative analysis indicators were standardized uptake value maximum (SUVmax) and the region of interest/liver ratio (R/L value). Receiver operating characteristic (ROCs) curve was used to determine the optimal cutoff values for SUVmax and R/L value. A clinical model was established using binary logistic regression, and ROC curves were drawn to evaluate the predictive performance of the model. Results:The uptake of 18F-FDG in the endometrium exhibited cyclical variations throughout different physiological phases, with higher uptakes observed during the menstrual and ovulation phases (SUVmax values of 6.66±3.26 and 3.89±1.21, respectively), which are significantly higher than those in the proliferative phase [median SUVmax of 2.54 (2.02, 3.47)], secretory phase (SUVmax of 2.55±0.86), and menopausal phase [SUVmax median of 2.04 (1.69, 2.29)]. During the menstrual and ovulation phases, the radiotracer accumulation patterns were triangular in 105 cases, oval in 32 cases, and round-like in 2 cases. All 42 cases of endometrial cancer showed 18F-FDG uptake, with radiotracer accumulation patterns being round-like in 17 cases, oval in 10 cases, triangular in 9 cases, and irregular in 6 cases. There were statistically significant differences in the shapes of radiotracer concentration between the menstrual, ovulatory periods, and endometrial carcinoma (both P＜0.001). The SUVmax and R/L values in menstrual period and ovulatory period were significantly lower than that in endometrial carcinoma group ( P＜0.001). During the menstrual phase, the optimal cutoff values for SUVmax and R/L in distinguishing between endometrial and endometrial cancer were 12.59 and 3.81, respectively, with corresponding AUCs of 0.885 and 0.842. After incorporating endometrial uptake morphology into the model, the AUCs was improved to 0.969 and 0.948, respectively. During the ovulatory phase, the optimal cutoff values for SUVmax and R/L were 5.96 and 2.85, respectively, with AUCs of 0.984 and 0.968. After integrating endometrial uptake morphology into the model, the AUCs were increased to 0.999 and 0.998, respectively. Conclusions:The 18F-FDG PET imaging of the endometrium shows higher uptake during the menstrual and ovulatory periods, primarily triangular in shape; endometrial carcinoma uptake is significantly higher than the physiological uptake during the menstrual and ovulatory periods, mainly in circular, oval, and irregular shapes. When SUVmax≥5.96, R/L≥2.85, combined with the physiological cycle of the subjects and the morphological characteristics of the radiotracer concentration, it is possible to effectively differentiate between physiological uptake and Stage IA endometrial carcinoma.

Objective:To explore the value of a model combining 18F-FDG PET/CT radiomics and clinical factors in prediction of lymphovascular invasion (LVI) in rectal cancer. Methods:This retrospective cohort study was conducted on 120 patients (86 males and 34 females; age (62.2±11.6) years) with rectal adenocarcinoma from the Affiliated Hospital of Qingdao University between January 2017 and November 2023. Patients were divided into a training set ( n=96) and testing set ( n=24) at the ratio of 8∶2 using simple random sampling without replacement with a fixed random seed. An external validation cohort consisted of 31 patients (17 males and 14 females; age (61.2±8.2) years) with rectal adenocarcinoma from Affiliated Hospital of Jining Medical University and Linyi Cancer Hospital between January 2020 and June 2024 was obtained. PET/CT-derived features were selected to build radiomics model. The χ2 test and logistic regression were used to identify clinical predictors of LVI for clinical modeling. A combined radiomics-clinical nomogram was developed, after that ROC analysis was conducted to evaluate the predictive performance. Results:Significant differences were found between LVI-positive ( n=40) and LVI-negative ( n=56) subgroups in body weight, carbohydrate antigen (CA) 19-9, metabolic tumor volume (MTV), and peak of SUV (SUV peak) in the training set ( χ2 values: 4.01-13.64, all P<0.05). Binary logistic regression identified body weight (odds ratio ( OR)=0.320, 95% CI: 0.095-0.906, P=0.033), CA19-9 ( OR=0.402, 95% CI: 0.120-0.917, P=0.033), and MTV ( OR=0.192, 95% CI: 0.090-0.575, P=0.002) as independent predictors of LVI, forming the clinical model. Thirteen PET features and fifteen CT features were selected and a radiomics model was built. ROC curve analysis showed that AUCs for the clinical model in the training, testing, and external validation sets were 0.765, 0.567, and 0.777, respectively; AUCs for the radiomics model were 0.925, 0.881, and 0.823; AUCs for the joint model were 0.938, 0.889, and 0.841. Conclusion:The joint model of 18F-FDG PET/CT radiomics and clinical factors can effectively predict LVI in rectal cancer, guiding preoperative therapy and surgical planning.

Globally， the incidence and mortality rates of liver cancer rank among the highest， posing a serious threat to the life and health of people. This article elaborates on the principles and characteristics of multimodal image fusion technology and artificial intelligence technology， analyzes their specific application in liver surgery， and highlights their clinical value and future development prospects， in order to provide a reference for the surgical diagnosis and treatment of liver diseases.

Yttrium-90(90Y)selective internal radiation therapy(SIRT)is an emerging modality for the treatment of hepatocellular carcinoma(HCC),leveraging the nuclide 90Y to deliver targeted radiation therapy.90Y has a long half-life and can be used to selectively ablate tumor cells by high-energy beta rays.It has high biological effectiveness and robust local control capabilities.In recent years,with the continuous advancement of basic and clinical research,the application of 90Y-SIRT in the conversion treatment of unresectable HCC(uHCC)has made significant progress.However,challenges remain in the clinical application of 90Y-SIRT,including how to improve the efficacy of conversion therapy and how to optimize therapy regimens.This review aims to summarize the research progress of90Y-SIRT in the conversion therapy of uHCC.

Objective:To establish a serum detection method of surface-enhanced Raman spectroscopy (SERS) combining with machine learning for early screening of colorectal cancer (CRC).Methods:Serum samples were collected from 150 CRC patients diagnosed at Jiangdu People′s Hospital, Affiliated to Yangzhou University, and also from 37 healthy subjects. Gold nanohexapod (AuNHs) arrays were prepared using an oil-water interface self-assembly method. A 5 μl serum sample was applied onto the AuNHs array. Scatheless and rapid detection for serum were performed using a Renishaw inVia Raman spectrometer at room temperature with a laser wavelength of 785 nm, exposure time of 10 s, and power of 5 mW. The raw SERS spectra were preprocessed using Savitzky-Golay smoothing, AsLS baseline correction, and Min-Max normalization with Origin 2019 software. Furthermore, the principal component analysis (PCA)-support vector machine (SVM) model was constructed using Python′s scikit-learn library. Leave-One-Out Cross-Validation (LOOCV) was used to evaluate the model′s accuracy, sensitivity, specificity, and area under the curve (AUC).Results:The AuNHs arrays exhibited uniform morphology. The relative standard deviation (RSD) of the SERS intensity at 1 080 cm -1 was 5.69%, and the RSD of the SERS intensity at 1 340 cm -1 was 6.20%. The limit of detection (LOD) of the AuNHs array was 9.42×10 -12 mol/L. The PCA-SVM model achieved an accuracy of 90.91% (170/187), sensitivity of 96.79% (181/187), specificity of 99.47% (186/187), and an AUC of 0.98. The most significant characteristic peaks distinguishing different CRC stages were at 747, 940, 1 000, 1 447, and 1 612 cm -1. Conclusion:The serum detection method based on SERS combined with machine learning can accurately screen CRC with higher accuracy, sensitivity, and specificity, demonstrating potential clinical application value.

Objective:To explore the value of a model combining 18F-FDG PET/CT radiomics and clinical factors in prediction of lymphovascular invasion (LVI) in rectal cancer. Methods:This retrospective cohort study was conducted on 120 patients (86 males and 34 females; age (62.2±11.6) years) with rectal adenocarcinoma from the Affiliated Hospital of Qingdao University between January 2017 and November 2023. Patients were divided into a training set ( n=96) and testing set ( n=24) at the ratio of 8∶2 using simple random sampling without replacement with a fixed random seed. An external validation cohort consisted of 31 patients (17 males and 14 females; age (61.2±8.2) years) with rectal adenocarcinoma from Affiliated Hospital of Jining Medical University and Linyi Cancer Hospital between January 2020 and June 2024 was obtained. PET/CT-derived features were selected to build radiomics model. The χ2 test and logistic regression were used to identify clinical predictors of LVI for clinical modeling. A combined radiomics-clinical nomogram was developed, after that ROC analysis was conducted to evaluate the predictive performance. Results:Significant differences were found between LVI-positive ( n=40) and LVI-negative ( n=56) subgroups in body weight, carbohydrate antigen (CA) 19-9, metabolic tumor volume (MTV), and peak of SUV (SUV peak) in the training set ( χ2 values: 4.01-13.64, all P<0.05). Binary logistic regression identified body weight (odds ratio ( OR)=0.320, 95% CI: 0.095-0.906, P=0.033), CA19-9 ( OR=0.402, 95% CI: 0.120-0.917, P=0.033), and MTV ( OR=0.192, 95% CI: 0.090-0.575, P=0.002) as independent predictors of LVI, forming the clinical model. Thirteen PET features and fifteen CT features were selected and a radiomics model was built. ROC curve analysis showed that AUCs for the clinical model in the training, testing, and external validation sets were 0.765, 0.567, and 0.777, respectively; AUCs for the radiomics model were 0.925, 0.881, and 0.823; AUCs for the joint model were 0.938, 0.889, and 0.841. Conclusion:The joint model of 18F-FDG PET/CT radiomics and clinical factors can effectively predict LVI in rectal cancer, guiding preoperative therapy and surgical planning.

Objective:To establish a serum detection method of surface-enhanced Raman spectroscopy (SERS) combining with machine learning for early screening of colorectal cancer (CRC).Methods:Serum samples were collected from 150 CRC patients diagnosed at Jiangdu People′s Hospital, Affiliated to Yangzhou University, and also from 37 healthy subjects. Gold nanohexapod (AuNHs) arrays were prepared using an oil-water interface self-assembly method. A 5 μl serum sample was applied onto the AuNHs array. Scatheless and rapid detection for serum were performed using a Renishaw inVia Raman spectrometer at room temperature with a laser wavelength of 785 nm, exposure time of 10 s, and power of 5 mW. The raw SERS spectra were preprocessed using Savitzky-Golay smoothing, AsLS baseline correction, and Min-Max normalization with Origin 2019 software. Furthermore, the principal component analysis (PCA)-support vector machine (SVM) model was constructed using Python′s scikit-learn library. Leave-One-Out Cross-Validation (LOOCV) was used to evaluate the model′s accuracy, sensitivity, specificity, and area under the curve (AUC).Results:The AuNHs arrays exhibited uniform morphology. The relative standard deviation (RSD) of the SERS intensity at 1 080 cm -1 was 5.69%, and the RSD of the SERS intensity at 1 340 cm -1 was 6.20%. The limit of detection (LOD) of the AuNHs array was 9.42×10 -12 mol/L. The PCA-SVM model achieved an accuracy of 90.91% (170/187), sensitivity of 96.79% (181/187), specificity of 99.47% (186/187), and an AUC of 0.98. The most significant characteristic peaks distinguishing different CRC stages were at 747, 940, 1 000, 1 447, and 1 612 cm -1. Conclusion:The serum detection method based on SERS combined with machine learning can accurately screen CRC with higher accuracy, sensitivity, and specificity, demonstrating potential clinical application value.

Objective:To investigate the uptake patterns of 18F-fluorodeoxy glucose ( 18F-FDG) in the endometrium using positron emission tomography (PET) imaging and to differentiate these from stage IA endometrial cancer. Methods:From September 2022 to April 2024, a prospective inclusion of 354 women without gynecological diseases and no hormone usage who underwent 18F-FDG PET-CT examinations at the affiliated hospital of Qingdao University were set as the physiological group, while a group containing 42 cases of Stage IA endometrial carcinoma was also set. The physiological group was divided into five groups based on the menstrual cycle: menstrual period, proliferative phase, ovulatory phase, secretory phase, and menopausal phase. The images were analyzed using visual and quantitative measurements; quantitative analysis indicators were standardized uptake value maximum (SUVmax) and the region of interest/liver ratio (R/L value). Receiver operating characteristic (ROCs) curve was used to determine the optimal cutoff values for SUVmax and R/L value. A clinical model was established using binary logistic regression, and ROC curves were drawn to evaluate the predictive performance of the model. Results:The uptake of 18F-FDG in the endometrium exhibited cyclical variations throughout different physiological phases, with higher uptakes observed during the menstrual and ovulation phases (SUVmax values of 6.66±3.26 and 3.89±1.21, respectively), which are significantly higher than those in the proliferative phase [median SUVmax of 2.54 (2.02, 3.47)], secretory phase (SUVmax of 2.55±0.86), and menopausal phase [SUVmax median of 2.04 (1.69, 2.29)]. During the menstrual and ovulation phases, the radiotracer accumulation patterns were triangular in 105 cases, oval in 32 cases, and round-like in 2 cases. All 42 cases of endometrial cancer showed 18F-FDG uptake, with radiotracer accumulation patterns being round-like in 17 cases, oval in 10 cases, triangular in 9 cases, and irregular in 6 cases. There were statistically significant differences in the shapes of radiotracer concentration between the menstrual, ovulatory periods, and endometrial carcinoma (both P＜0.001). The SUVmax and R/L values in menstrual period and ovulatory period were significantly lower than that in endometrial carcinoma group ( P＜0.001). During the menstrual phase, the optimal cutoff values for SUVmax and R/L in distinguishing between endometrial and endometrial cancer were 12.59 and 3.81, respectively, with corresponding AUCs of 0.885 and 0.842. After incorporating endometrial uptake morphology into the model, the AUCs was improved to 0.969 and 0.948, respectively. During the ovulatory phase, the optimal cutoff values for SUVmax and R/L were 5.96 and 2.85, respectively, with AUCs of 0.984 and 0.968. After integrating endometrial uptake morphology into the model, the AUCs were increased to 0.999 and 0.998, respectively. Conclusions:The 18F-FDG PET imaging of the endometrium shows higher uptake during the menstrual and ovulatory periods, primarily triangular in shape; endometrial carcinoma uptake is significantly higher than the physiological uptake during the menstrual and ovulatory periods, mainly in circular, oval, and irregular shapes. When SUVmax≥5.96, R/L≥2.85, combined with the physiological cycle of the subjects and the morphological characteristics of the radiotracer concentration, it is possible to effectively differentiate between physiological uptake and Stage IA endometrial carcinoma.

Objective:To explore the value of 18F-fibroblast activation protein inhibitor (FAPI) PET/CT in the assessment of early-stage graft fibrosis (S1-S2) after liver transplantation (LT). Methods:From November 2021 to April 2022, 17 adult liver transplant recipients (12 males and 5 females; age (52.6±7.9) years) in the Affiliated Hospital of Qingdao University were enrolled retrospectively in this study. All 17 patients received laboratory examinations, FibroScan, 18F-FAPI PET/CT and liver biopsy. According to the Scheuer scoring system, hepatic tissue was divided into no fibrosis (S0) and early fibrosis (S1-S2). Independent-sample t test was used to compare SUV max between two groups, and Mann-Whitney U test was used to compare liver stiffness measurement (LSM). ROC curve analysis was used to evaluate the diagnostic efficacy of LSM and SUV max in the early fibrosis of liver grafts. Delong test was used to compare the difference of AUCs. Results:Among 17 adult LT recipients, 11 were in stage S0, 5 were in stage S1, and 1 was in stage S2. There were significant differences in LSM and SUV max between no fibrosis group and early fibrosis group (LSM: 5.4(4.7, 6.6) vs 12.9(5.6, 19.9) kPa, z=-2.01, P=0.044; SUV max: 1.7±0.8 vs 3.9±1.6, t=-3.14, P=0.019). The threshold value of LSM in predicting early-stage graft fibrosis was 8.2 kPa and the AUC was 0.80 (95% CI: 0.54-0.95), which was 2.0 and 0.92 (95% CI: 0.78-1.00) for SUV max respectively. There was no significant difference in AUC between the two tools ( z=0.80, P=0.421). Conclusion:18F-FAPI PET/CT can precisely evaluate the early fibrosis of allografts, with the similar diagnostic efficacy with FibroScan (LSM), which is expected to be a new non-invasive diagnostic tool for predicting the early-stage of graft liver fibrosis.

Objective:To estimate the influence of metabolic parameters in 18F-FDG PET/CT and clinically relevant indicators on the prognosis of patients with cervical cancer. Methods:A total of 174 patients with cervical cancer (age (53.6±11.1) years) who underwent baseline 18F-FDG PET/CT examination in the Affiliated Hospital of Qingdao University from May 2011 to December 2020 were retrospectively collected. Metabolic parameters (metabolic tumor volume of primary lesion (MTV p), total lesion glycolysis of primary lesion (TLG p), MTV sum of total lesions (MTV total) in the whole body, TLG sum of total lesions (TLG total)) and clinical parameters (International Federation of Gynecology and Obstetrics (FIGO) stage, tumor maximum diameter ( Dmax), et al) were collected. Cox regression and Kaplan-Meier method were performed to evaluate the prognostic and predictive values of those parameters. Results:The follow-up time was 6-120 months, during which 52 patients (29.9%, 52/174) developed progression. The 5-year overall survival (OS), progression-free survival (PFS), local control (LC) and distant metastasis-free survival (DMFS) rates were 83.3%(145/174), 70.1%(122/174), 75.3%(131/174) and 82.8%(144/174), respectively. Cox regression showed that FIGO stage and MTV total were independent factors for predicting PFS, OS and LC (hazard ratio ( HR): 1.005-11.605, all P<0.05). FIGO stage and TLG total were independent factors for predicting DMFS ( HR: 1.002-12.258, all P<0.05). Conclusion:MTV total and FIGO stage are effective predictors of patients with cervical squamous cell carcinoma.