Objective:To evaluate the clinical prognostic value of intratumoral metabolic heterogeneity parameters of baseline 18F-FDG PET/CT in primary cutaneous malignant melanoma (CMM). Methods:From October 2015 to July 2023, the clinical data of 35 patients (24 males, 11 females, age (66.6±13.1) years) diagnosed with primary CMM who underwent baseline 18F-FDG PET/CT in Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School were retrospectively analyzed. Conventional metabolic parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG)) and PET intratumoral metabolic heterogeneity parameters (area under the cumulative SUV histograms curve (AUC-CSH), linear regression slope, SUV max/ SUV mean) were assessed. Using thresholds of 30%, 40%, 50%, 60%, 70%, and 80% SUV max or thresholds of 40%, 60%, and 80% SUV max to delineate MTV, linear regression was performed, with the slopes being heterogeneity index-1 (HI-1) and heterogeneity index-2 (HI-2), respectively. Using SUV thresholds of 2.5, and 40%, 50%, 60%, and 70%SUV max to calculate AUC-CSH and SUV max/SUV mean. Kaplan-Meier survival curves and Cox proportional hazards models were used to analyze the prognostic value of primary lesion PET metabolic parameters on overall survival (OS) and progression-free survival (PFS). Results:The median follow-up time of 35 patients was 20 months, with 25 patients (71%) experiencing disease progression and 16 patients (46%) deceased. Multivariate Cox regression analysis revealed that HI-1, HI-2, MTV, SUV max/SUV mean2.5, and SUV max were the independent prognostic factors for PFS (hazard rate ( HR) (95% CI): 0.32(0.13-0.82), 0.32(0.13-0.82), 3.86(1.34-11.12), 4.61(1.33-16.02), 4.06(1.55-10.61), all P<0.05), whereas SUV max and SUV max/SUV mean2.5 were the independent prognostic factors for OS ( HR: 8.04(1.96-32.87), 2.87(1.09-7.51), P values: 0.004, 0.032). Conclusion:HI-1, HI-2, and SUV max/SUV mean2.5 have prognostic value for CMM, while the value of AUC-CSH heterogeneity parameters are not significant.

Objective:To establish a combined model of tumor heterogeneity metabolic parameters using 18F-FDG PET/CT and explore its value in differentiating benign from malignant pulmonary lesions. Methods:A total of 251 patients (157 males, 94 females; age 15-88 years) who were diagnosed with malignant lung lesions by 18F-FDG PET/CT and with definitive pathological results at Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2017 to February 2024 were retrospectively enrolled. Analysis was conducted on clinical data, traditional parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG)) of primary lesions on 18F-FDG PET/CT, and intra-tumoral metabolic heterogeneity index (HI; such as cumulative SUV volume histogram AUC (AUC-CSH), linear regression slope, CV). AUC-CSH and CV were calculated using SUV thresholds of 2.5 and 40%SUV max. Logistic univariate and multivariate regression analyses were used to extract independent predictors in clinical features and PET/CT parameters for the differential diagnosis of pulmonary lesions. A multi-parameter combined model was established through logistic regression and validated for diagnostic efficacy using ROC curve analysis. Results:Among 251 patients, 101 were benign and 150 were malignant. In univariate analysis, gender, age, tumor markers, spiculation sign, lobulation sign, vessel convergence sign, air bronchogram, long diameter, short diameter, SUV max, AUC-CSH 2.5, AUC-CSH 40%, CV2.5, and CV40% were predictive factors for the diagnosis of benign and malignant tumors (odds ratio ( OR): 0.57-17.39, all P<0.05). In multivariate analysis, gender, age, tumor markers, lobulation sign, vessel convergence sign, SUV max, AUC-CSH 40%, and CV40% were independent predictors for the diagnosis of benign and malignant tumors ( OR: 2.30-13.18, all P<0.05). The AUC, sensitivity, specificity, and accuracy of the multi-parameter combined model established with the above independent predictors were 0.89, 77.33%(116/150), 84.16%(85/101), 80.08%(201/251), respectively. Conclusion:18F-FDG PET/CT multi-parameter combined model has high value in the differentiation of benign and malignant pulmonary lesions.

Objective:To investigate the predictive value of 18F-FDG PET related metabolic parameters on microsatellite instability-high (MSI-H) and human epidermal growth factor receptor-2(HER2) expression in colorectal cancer (CRC). Methods:The 18F-FDG PET imaging, clinical and pathological data of 101 CRC patients (58 males, 43 females; age 68.0(58.0, 75.0) years) admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2016 to March 2024 were retrospectively collected, including 17 cases in MSI-H group, 84 cases in microsatellite stability/microsatellite instability-low (MSS/MSI-L) group, 23 cases in HER2 expression group, 34 cases in non-HER2 expression group, and 44 patients without HER2 detection. Univariate analyses (independent-sample t test, Mann-Whitney U test, χ2 test) and multivariate logistic regression analysis were used to screen out independent risk factors, and ROC curve was used to evaluate the predictive efficacy. Bootstrap method was used to verify the model internally. Results:There were significant differences in metabolic tumor volume (MTV) 80% between MSI-H group and MSS/MSI-L group (2.1(1.6, 4.0) vs 1.4(1.0, 2.7) cm 3;Z=-2.10, P=0.036), and total lesion glycolysis (TLG) 80% and carcinoembryonic antigen (CEA) were significantly different between those 2 groups ( Z=-2.27, χ2=6.40, both P<0.05). There were significant differences in TLG 80% (29.0(16.1, 41.0) vs 14.3(9.4, 22.9) g; Z=-2.80, P=0.005) between HER2 expression group and non-HER2 expression group, and significant differences were also found in MTV 80%, heterogeneity index (HI) and CV ( Z=-2.24, t values: -2.26, 2.54, all P<0.05). The independent risk factors for MSI-H were MTV 80% (odds ratio ( OR)=1.326, 95% CI: 1.015-1.733, P=0.038) and CEA ( OR=0.200, 95% CI: 0.056-0.706, P=0.012), with the AUC for the combined model of 0.730 (95% CI: 0.605-0.856), and the concordance index (C-index) of 0.716. The independent risk factors for HER2 expression were TLG 80% ( OR=1.037, 95% CI: 1.001-1.073, P=0.041) and CV ( OR=1.467, 95% CI: 1.073-2.005, P=0.016), with the AUC for the combined model of 0.775 (95% CI: 0.645-0.875), and the C-index of 0.757. Conclusions:18F-FDG PET can be used as a noninvasive tool to evaluate CRC microsatellite status and HER2 gene amplification. MTV 80% and CEA are independent risk factors for MSI-H; TLG 80% and CV are independent risk factors for HER2 expression.

Objective:To evaluate the clinical prognostic value of intratumoral metabolic heterogeneity parameters of baseline 18F-FDG PET/CT in primary cutaneous malignant melanoma (CMM). Methods:From October 2015 to July 2023, the clinical data of 35 patients (24 males, 11 females, age (66.6±13.1) years) diagnosed with primary CMM who underwent baseline 18F-FDG PET/CT in Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School were retrospectively analyzed. Conventional metabolic parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG)) and PET intratumoral metabolic heterogeneity parameters (area under the cumulative SUV histograms curve (AUC-CSH), linear regression slope, SUV max/ SUV mean) were assessed. Using thresholds of 30%, 40%, 50%, 60%, 70%, and 80% SUV max or thresholds of 40%, 60%, and 80% SUV max to delineate MTV, linear regression was performed, with the slopes being heterogeneity index-1 (HI-1) and heterogeneity index-2 (HI-2), respectively. Using SUV thresholds of 2.5, and 40%, 50%, 60%, and 70%SUV max to calculate AUC-CSH and SUV max/SUV mean. Kaplan-Meier survival curves and Cox proportional hazards models were used to analyze the prognostic value of primary lesion PET metabolic parameters on overall survival (OS) and progression-free survival (PFS). Results:The median follow-up time of 35 patients was 20 months, with 25 patients (71%) experiencing disease progression and 16 patients (46%) deceased. Multivariate Cox regression analysis revealed that HI-1, HI-2, MTV, SUV max/SUV mean2.5, and SUV max were the independent prognostic factors for PFS (hazard rate ( HR) (95% CI): 0.32(0.13-0.82), 0.32(0.13-0.82), 3.86(1.34-11.12), 4.61(1.33-16.02), 4.06(1.55-10.61), all P<0.05), whereas SUV max and SUV max/SUV mean2.5 were the independent prognostic factors for OS ( HR: 8.04(1.96-32.87), 2.87(1.09-7.51), P values: 0.004, 0.032). Conclusion:HI-1, HI-2, and SUV max/SUV mean2.5 have prognostic value for CMM, while the value of AUC-CSH heterogeneity parameters are not significant.

Objective:To establish a combined model of tumor heterogeneity metabolic parameters using 18F-FDG PET/CT and explore its value in differentiating benign from malignant pulmonary lesions. Methods:A total of 251 patients (157 males, 94 females; age 15-88 years) who were diagnosed with malignant lung lesions by 18F-FDG PET/CT and with definitive pathological results at Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2017 to February 2024 were retrospectively enrolled. Analysis was conducted on clinical data, traditional parameters (SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG)) of primary lesions on 18F-FDG PET/CT, and intra-tumoral metabolic heterogeneity index (HI; such as cumulative SUV volume histogram AUC (AUC-CSH), linear regression slope, CV). AUC-CSH and CV were calculated using SUV thresholds of 2.5 and 40%SUV max. Logistic univariate and multivariate regression analyses were used to extract independent predictors in clinical features and PET/CT parameters for the differential diagnosis of pulmonary lesions. A multi-parameter combined model was established through logistic regression and validated for diagnostic efficacy using ROC curve analysis. Results:Among 251 patients, 101 were benign and 150 were malignant. In univariate analysis, gender, age, tumor markers, spiculation sign, lobulation sign, vessel convergence sign, air bronchogram, long diameter, short diameter, SUV max, AUC-CSH 2.5, AUC-CSH 40%, CV2.5, and CV40% were predictive factors for the diagnosis of benign and malignant tumors (odds ratio ( OR): 0.57-17.39, all P<0.05). In multivariate analysis, gender, age, tumor markers, lobulation sign, vessel convergence sign, SUV max, AUC-CSH 40%, and CV40% were independent predictors for the diagnosis of benign and malignant tumors ( OR: 2.30-13.18, all P<0.05). The AUC, sensitivity, specificity, and accuracy of the multi-parameter combined model established with the above independent predictors were 0.89, 77.33%(116/150), 84.16%(85/101), 80.08%(201/251), respectively. Conclusion:18F-FDG PET/CT multi-parameter combined model has high value in the differentiation of benign and malignant pulmonary lesions.

Objective:To investigate the predictive value of 18F-FDG PET related metabolic parameters on microsatellite instability-high (MSI-H) and human epidermal growth factor receptor-2(HER2) expression in colorectal cancer (CRC). Methods:The 18F-FDG PET imaging, clinical and pathological data of 101 CRC patients (58 males, 43 females; age 68.0(58.0, 75.0) years) admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2016 to March 2024 were retrospectively collected, including 17 cases in MSI-H group, 84 cases in microsatellite stability/microsatellite instability-low (MSS/MSI-L) group, 23 cases in HER2 expression group, 34 cases in non-HER2 expression group, and 44 patients without HER2 detection. Univariate analyses (independent-sample t test, Mann-Whitney U test, χ2 test) and multivariate logistic regression analysis were used to screen out independent risk factors, and ROC curve was used to evaluate the predictive efficacy. Bootstrap method was used to verify the model internally. Results:There were significant differences in metabolic tumor volume (MTV) 80% between MSI-H group and MSS/MSI-L group (2.1(1.6, 4.0) vs 1.4(1.0, 2.7) cm 3;Z=-2.10, P=0.036), and total lesion glycolysis (TLG) 80% and carcinoembryonic antigen (CEA) were significantly different between those 2 groups ( Z=-2.27, χ2=6.40, both P<0.05). There were significant differences in TLG 80% (29.0(16.1, 41.0) vs 14.3(9.4, 22.9) g; Z=-2.80, P=0.005) between HER2 expression group and non-HER2 expression group, and significant differences were also found in MTV 80%, heterogeneity index (HI) and CV ( Z=-2.24, t values: -2.26, 2.54, all P<0.05). The independent risk factors for MSI-H were MTV 80% (odds ratio ( OR)=1.326, 95% CI: 1.015-1.733, P=0.038) and CEA ( OR=0.200, 95% CI: 0.056-0.706, P=0.012), with the AUC for the combined model of 0.730 (95% CI: 0.605-0.856), and the concordance index (C-index) of 0.716. The independent risk factors for HER2 expression were TLG 80% ( OR=1.037, 95% CI: 1.001-1.073, P=0.041) and CV ( OR=1.467, 95% CI: 1.073-2.005, P=0.016), with the AUC for the combined model of 0.775 (95% CI: 0.645-0.875), and the C-index of 0.757. Conclusions:18F-FDG PET can be used as a noninvasive tool to evaluate CRC microsatellite status and HER2 gene amplification. MTV 80% and CEA are independent risk factors for MSI-H; TLG 80% and CV are independent risk factors for HER2 expression.

Objective:To evaluate the value of a combined model based on primary lesion 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer (NSCLC) . Methods:A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set ( n=142) and the validation set ( n=61) at a ratio of 7∶3. A predictive model was developed in the training set, and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score (Radscore). Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic (ROC) curves and clinical application value was assessed by calibration curves and decision curve analysis (DCA) . Results:Among 203 NSCLC patients, 116 had lymph node metastasis, with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening, and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that, sex ( OR=0.48, 95% CI: 0.24-0.95, P=0.036), tumor marker levels ( OR=3.81, 95% CI: 1.84-7.91, P<0.001), long diameter of tumor ( OR=2.56, 95% CI: 1.27-5.16, P=0.009), short diameter of tumor ( OR=3.73, 95% CI: 1.75-7.92, P=0.001), vacuolar sign ( OR=0.32, 95% CI: 0.12-0.86, P=0.024), ring-like metabolism ( OR=3.67, 95% CI: 1.33-10.13, P=0.012), maximum standardized uptake value (SUV max) ( OR=6.57, 95% CI: 3.03-14.25, P<0.001), metabolic tumor volume (MTV) ( OR=2.91, 95% CI: 1.43-5.92, P=0.003), total lesion glycolysis (TLG) ( OR=4.23, 95% CI: 2.08-8.59, P<0.001), Radscore-PET ( OR=21.93, 95% CI: 9.04-53.20, P<0.001) and Radscore-CT ( OR=13.72, 95% CI: 6.12-30.76, P<0.001) were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that, tumor marker levels ( OR=2.55, 95% CI: 1.11-5.90, P=0.028), vacuolar sign ( OR=0.26, 95% CI: 0.08-0.83, P=0.023), SUV max ( OR=5.94, 95% CI: 1.99-17.75, P=0.001), Radscore-PET ( OR=25.51, 95% CI: 5.92-110.22, P<0.001), and Radscore-CT ( OR=8.68, 95% CI: 2.73-27.61, P<0.001) were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors, models were constructed: the traditional model (tumor marker levels, vacuolar sign, SUV max), the PET model (SUV max, Radscore-PET), the CT model (vacuolar sign, Radscore-CT), and the combined model (tumor marker levels, vacuolar sign, SUV max, Radscore-PET, Radscore-CT). ROC curve analysis showed that, the area under curve (AUC) of the traditional, PET, CT, and combined models in the training set were 0.75 (95% CI: 0.67-0.82), 0.90 (95% CI: 0.84-0.95), 0.85 (95% CI: 0.78-0.90), and 0.94 (95% CI: 0.88-0.97), respectively. The predictive value of the combined model was higher than that of the traditional model ( Z=5.01, P<0.001), the PET model ( Z=1.99, P=0.047), and the CT model ( Z=3.25, P=0.001). In the validation set, the AUCs for the traditional model, PET model, CT model, and combined model were 0.65 (95% CI: 0.52-0.77), 0.86 (95% CI: 0.74-0.93), 0.85 (95% CI: 0.73-0.93), and 0.90 (95% CI: 0.80-0.96), respectively. The predictive value of the combined model was superior to that of the traditional model ( Z=3.23, P=0.001). The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%, while in the validation set, the sensitivity and specificity were 82.61% and 94.74%, respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions:Tumor marker levels, vacuolar sign, SUV max, Radscore-PET, and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.

Objective:To evaluate the diagnostic value of multimodal Nomogram model combining 18F-FDG PET/CT and ultrasound for triple negative breast cancer (TNBC) . Methods:A total of 61 breast cancer patients admitted at Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from November 2016 to May 2024 were selected as the study subjects, including 12 cases of TNBC and 49 cases of non-TNBC. 18F-FDG PET/CT metabolic parameters maximum standardized uptake value (SUV max), mean standardized uptake value (SUV mean), minimum standardized uptake value (SUV min), tumor metabolic volume (MTV), and total lesion glycolysis (TLG), as well as the ultrasound parameters long diameter, short diameter, echogenicity, morphology, boundaries, posterior echogenicity, aspect ratio, microcalcifications, blood flow grading and Breast Imaging Reporting and Data System (BI-RADS) grading were compared between patients with and without TNBC. Least absolute shrinkage and selection operator (LASSO) regression was used for feature screening, and binary multivariate logistic regression analysis was conducted on the screened variables to obtain the independent influencing factors for diagnosing TNBC. The independent factors influencing the diagnosis of TNBC were established as Nomogram model and visualized. Receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the diagnostic efficacy, accuracy and clinical practicability of the model, respectively. Results:There were statistically significant differences in SUV max ( Z=-2.43, P=0.015), SUV mean ( Z=-2.54, P=0.011), morphology ( P=0.004), boundaries ( χ2=4.86, P=0.028), posterior echogenicity ( P=0.027), and blood flow grading ( χ2=4.52, P=0.034) between TNBC and non-TNBC patients. LASSO regression screened out three variables: SUV max, morphology and blood flow grading. Multivariate analysis showed that, SUV max ( OR=1.20, 95% CI: 1.04-1.38, P=0.012), morphology ( OR=0.02, 95% CI: 0.01-0.49, P=0.016), and blood flow grading ( OR=0.06, 95% CI: 0.01-0.74, P=0.028) were the independent influencing factors for diagnosing TNBC. A Nomogram model was established based on the above independent influencing factors. ROC curve showed that, area under the curve (AUC) of SUV max, morphology, blood flow grading, and the Nomogram model were 0.73 (95% CI: 0.60-0.83), 0.66 (95% CI: 0.52-0.77), 0.67 (95% CI: 0.54-0.79), 0.90 (95% CI: 0.79-0.96), respectively, and the diagnostic value of the Nomogram model was higher than that of SUV max ( Z=2.71, P=0.007), morphology ( Z=3.61, P<0.001), and blood flow grading ( Z=2.51, P=0.012) alone. Calibration curve and DCA showed better accuracy and clinical practicability of the Nomogram model. Conclusions:Nomogram model constructed by combining the SUV max of 18F-FDG PET/CT with the morphology and blood flow grading of ultrasound has a promising potential for diagnosing TNBC.

Objective:To evaluate the prognostic value of 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT metabolic parameters in small cell lung cancer (SCLC) . Methods:A retrospective analysis was conducted on the clinical and imaging data of 156 SCLC patients, who underwent 18F-FDG PET/CT imaging and were diagnosed by histopathological examination at Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from September 2013 to February 2024. The metabolic tumor volume (MTV), total lesion glycolysis (TLG), linear regression slope, area under the curve of cumulative standard uptake value (SUV) volume histogram (AUC-CSH), and coefficient of variation (CV) were calculated using LIFEx software with different SUV thresholds. Univariate and multivariate analyses were performed using Cox proportional hazards model. Patient stratification was based on the critical values determined by receiver operator characteristic (ROC) curve analysis. The survival curve was plotted using the Kaplan-Meier method and log-rank test was performed. Results:Univariate analysis showed that MTV 40% ( HR=2.91, 95% CI: 1.55-5.47, P=0.001), MTV 60% ( HR=2.31, 95% CI: 1.29-4.17, P=0.005), TLG 40% ( HR=2.07, 95% CI: 1.19-3.60, P=0.010), linear regression slope ( HR=0.45, 95% CI: 0.26-0.79, P=0.005), and CV 40% ( HR=0.27, 95% CI: 0.08-0.84, P=0.024) were factors affecting progression-free survival (PFS) in SCLC patients. MTV 40% ( HR=1.98, 95% CI: 1.22-3.22, P=0.005), MTV 60% ( HR=1.80, 95% CI: 1.12-2.88, P=0.015), MTV 80% ( HR=1.71, 95% CI: 1.08-2.74, P=0.024), TLG 40% ( HR=3.68, 95% CI: 1.59-8.49, P=0.002), linear regression slope ( HR=0.49, 95% CI: 0.30-0.80, P=0.004), and AUC-CSH 80% ( HR=0.44, 95% CI: 0.23-0.84, P=0.013) were found to be factors affecting overall survival (OS) in SCLC patients. Multivariate analysis revealed that MTV 40% ( HR=4.76, 95% CI: 1.11-20.50, P=0.036) was an independent factor influencing PFS, and TLG 40% ( HR=3.19, 95% CI: 1.02-9.92, P=0.046) was an independent factor influencing OS in SCLC patients. ROC curve analysis identified the optimal cutoff value for MTV 40% in predicting PFS as 5.5cm 3 and the optimal cutoff value for TLG 40% in predicting OS as 41.5 g in SCLC patients. Survival analysis showed that patients with MTV 40%≤5.5 cm 3 ( n=33) had a median PFS that was not reached, while patients with MTV 40%>5.5 cm 3 ( n=123) had a median PFS of 10.3 months, with a statistically significant difference ( χ2=12.09, P=0.001). For patients with TLG 40%≤41.5 g ( n=35), the median OS was not reached, whereas for TLG 40%>41.5 g ( n=121), the median OS was 31.6 months, with a statistically significant difference ( χ2=10.55, P=0.001) . Conclusions:The 18F-FDG PET/CT metabolic parameter MTV 40% is an independent factor influencing PFS, while TLG 40% is an independent factor influencing OS in SCLC patients. The above two parameters may serve as indicators for assessing the prognosis of SCLC patients.

Objective:To explore the value of preoperative 18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET) metabolic heterogeneity parameters in predicting tumor deposits (TD) in colorectal cancer (CRC). Methods:A retrospective analysis was conducted on 91 CRC patients who underwent surgical treatment at the Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2013 to March 2024. All patients underwent preoperative 18F-FDG PET/CT examination. The LIFEx-7.5.15 software was used to delineate the primary lesion with 40% of maximum standardized uptake value (SUV max) as the relative threshold, and metabolic parameters were extracted. Intratumoral metabolic heterogeneity parameters included cumulative SUV histogram area under the curve (AUC-CSH), heterogeneity index (HI), heterogeneity factor (HF), and coefficient of variation (CV). The presence of TD was confirmed by postoperative pathological examination. Differences in data between the TD group and non-TD (NTD) group were compared. Binary logistic regression analysis was used to identify independent risk factors for TD, and receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of each parameter for TD. Results:Postoperative pathological diagnosis showed that 27 patients were included in the TD group and 64 in the NTD group. There were statistically significant differences between the TD group and NTD group in CV ( Z=-3.145, P=0.002) and the proportion of patients with carcinoembryonic antigen (CEA) >10 ng/ml (χ 2=10.751, P=0.001), while no statistically significant differences were found in HI, HF, or AUC-CSH (all P>0.05). Binary logistic regression analysis showed that CV was an independent risk factor for TD. ROC curve analysis showed that the area under the ROC curve (AUC) of CV for predicting TD was 0.709(95% CI: 0.593-0.826), which was higher than that of other metabolic heterogeneity parameters. Conclusions:The preoperative 18F-FDG PET/CT metabolic heterogeneity parameter CV has value in predicting TD in CRC patients.