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 assess the prognostic value of pretreatment 18F-FDG PET/CT metabolic parameters in patients with metastatic malignant melanoma treated with anti-programmed cell death-1 (PD1) immunotherapy. Methods:A retrospective analysis of 29 patients (15 males, 14 females, age (59.1±13.0) years) with pathologically diagnosed metastatic malignant melanoma in Nanjing Drum Tower Hospital between June 2017 and October 2020 was conducted. Anti-PD1 immunotherapy were performed in all patients after 18F-FDG PET/CT imaging. 18F-FDG PET/CT parameters including SUV max, bone marrow-to-liver SUV max ratio (BLR), spleen-to-liver SUV max ratio (SLR) were obtained. Total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) of primary lesions were measured automatically using the thresholds of 40%SUV max. The median value of each PET parameter was regarded as the threshold value and was used to divide patients into 2 groups (≥ and < the median value, respectively). Kaplan-Meier survival curve and Cox proportional risk model were used to analyze the overall survival (OS) differences between groups. Results:The median follow-up time was 15.0 months and 13 patients died. The median OS was 26.0(95% CI: 20.4-31.6) months. The median SUV max, TMTV, TLG, BLR and SLR were 6.2, 8.2 cm 3, 38.6 g, 0.82 and 0.84 respectively. Kaplan-Meier method and log-rank test showed that differences of OS between SUV max≥6.2 and <6.2 groups, TLG≥38.6 g and <38.6 g groups, BLR≥0.82 and <0.82 groups, SLR≥0.84 and <0.84 groups were not significant ( χ2 values: 0.01-0.35, P values: 0.061-0.929), while patients with TMTV≥8.2 cm 3 suffered from poorer OS compared with those with TMTV<8.2 cm 3 ( χ2=5.90, P=0.015). Cox multivariate analysis showed that TMTV (hazard risk ( HR)=6.347, 95% CI: 1.039-38.789) was a significant predictor of OS ( P=0.045). Conclusion:18F-FDG PET/CT parameter TMTV is the independent predictive factor of OS in metastatic melanoma treated with anti-PD1 immunotherapy.

Objective:To investigate the prognostic value of metabolic parameters measured by 18F-FDG PET/CT in patients with primary advanced cutaneous malignant melanoma (CMM). Methods:A retrospective analysis was comprised of 42 patients with advanced CMM (15 males and 27 females; median age: 60.0 years) from Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School between June 2014 and December 2019. All patients were initially diagnosed by pathology, and underwent 18F-FDG PET/CT imaging. 18F-FDG PET/CT parameters including SUV max, SUV mean, total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) of metastatic lesions were measured. ROC curve analysis was performed to obtain the optimal cut-off values of those metabolic parameters for predicting progression-free survival (PFS) and over-all survival (OS). Patients were divided into different groups based on their metabolic parameters (≥cut-off values or

Objective:To explore whether baseline PET metabolic parameters combined with B-cell lymphoma-2 (Bcl-2)/cellular-myelocytomatosis viral oncogene (c-Myc) dual expression (DE) can improve the prognostic stratification of patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:From March 2011 to November 2019, 74 patients (33 males, 41 females; age: 20-87 years) pathologically diagnosed with PGI-DLBCL prior to treatment in Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School and the First Affiliated Hospital of Nanjing Medical University were retrospectively included. Baseline PET/CT scans were calculated automatically using the boundaries of voxels presenting a SUV max≥2.5, and metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were determined. Expressions of Bcl-2 and c-Myc were detected at protein levels by immunohistochemistry (IHC). A predicting model comprised of MTV and DE was constructed and patients were divided into 3 groups, including low-risk group (low MTV and non-DE), mediate-risk group (high MTV or DE) and high-risk group (high MTV and DE). The distributions of progression-free survival (PFS) and overall survival (OS) rates were estimated using the Kaplan-Meier method, log-rank test and Cox proportional hazards model. Results:Of 74 patients, 20 relapsed or progressed, 13 died, and 29.7%(22/74) patients were DE positive. Multivariate analysis revealed that MTV (hazard ratio ( HR)=9.110, 95% CI: 1.429-18.615, P=0.012) and DE ( HR=9.837, 95% CI: 1.690-57.260, P=0.011) were independent predictors of PFS, while MTV ( HR=12.470, 95% CI: 3.356-46.336, P<0.001) was the only independent predictor of OS. In the predicting model for PFS, low-risk group ( n=42) and mediate-risk group ( n=20) exhibited significant difference ( χ2=7.84, P=0.005), and mediate-risk group and high-risk group ( n=12) also exhibited significant difference ( χ2=18.72, P<0.001). Conclusions:MTV and DE can independently predict PFS of patients with PGI-DLBCL, and MTV can independently predict OS. The predicting model for PFS combining MTV with DE may further improve the ability of clinicians to stratify patients in terms of differential prognoses.

Objective:To explore the value of 18F-FDG PET/CT in detecting Richter syndrome (RS) in chronic lymphocytic leukemia (CLL) patients. Methods:From August 2010 to November 2019, 101 histologically confirmed CLL patients (62 males, 39 females; age (58.0±12.7) years) who underwent PET/CT in Nanjing Drum Tower Hospital and the First Affiliated Hospital of Nanjing Medical University were retrospectively included. ROI was drawn and PET/CT images were semi-quantitatively examined by estimating SUV max. Mann-Whitney U test was used to compare the SUV max of RS and non-RS patients. ROC curve analysis was utilized to analyze the optimal cut-off value of SUV max in detecting RS. Results:RS was histologically confirmed in 27 CLL patients. The SUV max of RS patients was 13.7(11.0, 20.1), which was significantly higher than that of non-RS patients (4.1(3.1, 5.8); z=-6.48, P<0.001). ROC curve analysis identified the optimal cut-off value of SUV max was 10.0 and the AUC was 0.923, with accuracy of 94.1%(95/101), sensitivity of 85.2%(23/27), specificity of 97.3%(72/74), positive predictive value of 92.0%(23/25) and negative predictive value of 94.7%(72/76). Conclusion:As the semi-quantitative index measured by 18F-FDG PET/CT, SUV max can help to diagnose RS and provide important information for clinical use.

Objective:To assess the prognostic value of pretreatment 18F-FDG PET/CT metabolic parameters in patients with primary melanoma. Methods:A retrospective analysis comprised of 35 patients (21 males, 14 females, age: 35-85 years; from January 2014 to August 2019; Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School) who were newly-diagnosed primary melanoma with preoperative 18F-FDG PET/CT was conducted. 18F-FDG PET/CT metabolic parameters including SUV max, SUV mean, peak of SUV (SUV peak) were obtained. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary focus were measured automatically using the threshold of 40%SUV max. The optimal thresholds of PET parameters were obtained by using ROC curve analysis. The associations between melanoma-specific survival (MSS), progression-free survival (PFS) and PET/CT metabolic parameters were assessed using Kaplan-Meier method and Cox proportional hazards model. Results:The median follow-up was 15.4 months, and 20 patients showed disease progression and 7 died. The cut-off values for SUV max, SUV mean, SUV peak, MTV and TLG were 3.95, 2.45, 2.65, 3.60 cm 3 and 14.85 g, respectively (AUCs: 0.742, 0.790, 0.728, 0.655, 0.693; P values: 0.016, 0.004, 0.022, 0.121, 0.053). Kaplan-Meier method and log-rank test showed that SUV max, SUV mean, SUV peak, MTV and TLG were predictors of PFS ( χ2 values: 4.06-8.35, all P<0.05). Multivariate analysis showed that MTV (hazard ratio ( HR)=3.09, 95% CI: 1.08-8.86, P=0.036) and TLG ( HR=3.36, 95% CI: 1.11-10.14, P=0.031) were significant predictors of PFS but not for MSS ( HR=5.14, P=0.080). Conclusions:SUV max, SUV mean and SUV peak of primary focus may help for predicting PFS of patients with primary melanoma. MTV and TLG of primary focus may be the best to predict PFS of primary melanoma.

Objective:To explore the prognostic role of baseline 18F-FDG PET/CT metabolic parameters for patients with peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). Methods:From February 2010 to January 2019, 47 PTCL-NOS patients (29 males, 18 females, age: (59.7±13.6) years) from Nanjing Drum Tower Hospital were retrospectively enrolled. Each patient underwent baseline 18F-FDG PET/CT imaging before treatment. The total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) were computed by using the margin threshold of 41% SUV max. Kaplan-Meier survival analysis, univariate and multivariate Cox proportional hazards regression models were used to evaluate progression-free survival (PFS) and overall survival (OS). Results:Over the follow-up of 5-119 months, 25 patients had disease progression, including 24 deaths. SUV max (hazard ratio ( HR)=8.581, 95% CI: 1.950-37.764, P=0.004), TMTV( HR=9.677, 95% CI: 3.521-26.593, P<0.001), TLG( HR=3.647, 95% CI: 1.245-10.682, P<0.001) and prognostic index for T-cell lymphoma (PIT; HR=4.593, 95% CI: 1.792-11.773, P=0.002) were significant predictors of PFS and OS( HR=8.720, 95% CI: 1.982-83.354, P=0.004; HR=9.325, 95% CI: 3.423-25.408, P<0.001; HR=3.439, 95% CI: 1.170-10.110, P<0.001; HR=4.437, 95% CI: 1.728-11.393, P=0.002). After multivariate analysis, TMTV was the independent predictor of PFS ( HR=4.371, 95% CI: 1.066-16.541, P<0.001) and OS ( HR=4.978, 95% CI: 1.123-21.329, P<0.001). The substratification analysis showed that patients with high TMTV(≥168.3 cm 3) had worse prognosis than those with low TMTV (<168.3 cm 3) for PFS ( χ2=14.60, P<0.001) and OS ( χ2=16.81, P<0.001) in low PIT (0-1) group, while patients with high TMTV had worse prognosis than those with low TMTV for PFS ( χ2=4.09, P=0.043) in high PIT (≥2) group. Conclusions:Baseline PET/CT metabolic parameters including SUV max, TMTV, TLG and PIT are able to predict survival in PTCL-NOS patients. TMTV is the independent predictor of PFS and OS, which can substratify PTCL-NOS patients in PIT group.

Objective:To explore the potential value of interim 18F-fluorodeoxyglucose (FDG) PET/CT combined with B-cell lymphoma-2 (Bcl-2)/MYC protein dual expression (DE) status in the prognostic stratification for patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL). Methods:Forty-six patients (21 males, 25 females; age 20-83 years) with newly diagnosed PGI-DLBCL from June 2012 to May 2019 in Nanjing Drum Tower Hospital were enrolled in this retrospective study. Immunohistochemistry for Bcl-2 and MYC protein expression was performed. All patients underwent baseline and interim (after 2-4 cycles of cyclophosphamide, doxorubicin, vincristine, prednisone, and rituximab (R-CHOP) regimen) 18F-FDG PET/CT scans for assessment. Interim 18F-FDG PET/CT results were determined based on Deauville 5-point scale (DS) and changing rate of maximum standardized uptake value (ΔSUV max%) in 18F-FDG PET/CT images. Kaplan-Meier survival analysis, Cox proportional hazards regression model (single factor, multiple factors analysis) were used to analyze the prognosis (3-year progression free survival (PFS) and overall survival (OS) rates). Results:Patients were followed up for 6-84 months, and 14 showed disease progression and 9 died. The PFS rate and OS rate were 69.6% and 80.4%, respectively. DE, DS as well as ΔSUV max% were significant predictors of PFS (hazard ratio ( HR) values: 3.280, 5.120, 9.167, all P<0.05); lactate dehydrogenase (LDH), MYC protein expression, DS and ΔSUV max% were significant predictors of OS ( HR values: 4.091, 9.618, 7.697, 11.151, all P<0.05). Multivariate analysis revealed that DS and ΔSUV max% were independent predictors of PFS and OS ( HR values: 4.370-9.244, all P<0.05). In the DS negative (-) group, patients with DE positive (+ ) had lower PFS and OS rates than those with DE- (PFS rate: 50.0% vs 88.9%; OS rate: 66.7% vs 96.3%; χ2 values: 6.050, 4.966, both P<0.05). In ΔSUV max%<90% group, patients with DE+ had lower PFS rate than those with DE- (12.5% vs 68.8%; χ2=6.649, P=0.01). Conclusions:Interim PET/CT analysis using DS and ΔSUV max% is able to predict survival in PGI-DLBCL patients. The combination of DS, ΔSUV max% and DE can risk-stratify PGI-DLBCL patient more effectively.