Pretreatment Primary Tumor and Nodal SUVmax Values on 18F-FDG PET/CT Images Predict Prognosis in Patients With Salivary Gland Carcinoma

A multicenter retrospective analysis of patients with salivary gland carcinoma treated with postoperative radiotherapy alone or chemoradiotherapy

BACKGROUND

The aim of this study was to interrogate if the use of postoperative chemoradiotherapy (POCRT) correlated with superior oncological outcomes for certain subgroups of patients with high-risk salivary gland carcinoma (SGC), compared with postoperative radiotherapy (PORT) alone.

METHODS

This multicenter retrospective study included 411 patients with surgically resected SGC who underwent PORT (n = 263) or POCRT (n = 148) between 2000 and 2015. Possible correlations of clinical parameters with outcomes were examined using the Kaplan-Meier analysis and Cox proportional-hazards regression model.

RESULTS

The median follow-up of survivors is 10.9 years. For the entire cohort, adding concurrent chemotherapy to PORT was not associated with OS, PFS, or LRC improvement. However, patients with nodal metastasis who underwent POCRT had significantly higher 10-year OS (46.2% vs. 18.2%, P = 0.009) and PFS (38.7% vs. 10.0%, P = 0.009) rates than those treated with PORT alone. The presence of postoperative macroscopic residual tumor (R2 resection) was identified as an independent prognosticator for inferior OS (P = 0.032), PFS (P = 0.001), and LRC (P = 0.007). Importantly, POCRT significantly correlated with higher 10-year LRC rates in patients with R2 resection (74.2% vs. 40.7%, P = 0.034) or adenoid cystic carcinoma (AdCC, 97.6% vs. 83.6%, P = 0.039). On multivariate analyses, the use of POCRT significantly predicted superior OS (P = 0.037) and PFS (P = 0.013) for node-positive patients and LRC for patients with R2 resection (P = 0.041) or AdCC (P = 0.005).

CONCLUSIONS

For surgically resected SGC, POCRT was associated with improved long-term OS and PFS for patients with nodal metastasis and superior LRC for patients with R2 resection or AdCC.

18F-FDG-PET/CT-based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery

BACKGROUND

The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on ¹⁸F-fluorodeoxyglucose-positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer.

METHODS

A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent ¹⁸F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation.

RESULTS

The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively.

CONCLUSION

To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making.

Multiparameter diagnostic model based on 18F-FDG PET metabolic parameters and clinical variables can differentiate nonmetastatic gallbladder cancer and cholecystitis

OBJECTIVE

To evaluate the diagnostic value of a multiparameter model based on ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) metabolic parameters and clinical variables in differentiating nonmetastatic gallbladder cancer (GBC) from cholecystitis.

PATIENTS AND METHODS

In total, 122 patients (88 GBC nonmetastatic patients and 34 cholecystitis patients) with gallbladder space-occupying lesions who underwent ¹⁸F-FDG PET/CT were included. All patients received surgery and pathology, and baseline characteristics and clinical data were also collected. The metabolic parameters of ¹⁸F-FDG PET, including SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), SUVpeak (peak standard uptake value), MTV (metabolic tumour volume), TLG (total lesion glycolysis) and SUVR (tumour-to-normal liver standard uptake value ratio), were evaluated. The differential diagnostic efficacy of each independent parameter and multiparameter combination model was evaluated using the receiver operating characteristic (ROC) curve. The improvement in diagnostic efficacy using a combination of the above multiple parameters was evaluated by integrated discriminatory improvement (IDI), net reclassification improvement (NRI) and bootstrap test. Decision curve analysis (DCA) was used to evaluate clinical efficacy.

RESULTS

The ROC curve showed that SUVR had the highest diagnostic ability among the ¹⁸F-FDG PET metabolic parameters (area under the curve [AUC] = 0.698; sensitivity = 0.341; specificity = 0.971; positive predictive value [PPV] = 0.968; negative predictive value [NPV] = 0.363). The combined diagnostic model of cholecystolithiasis, fever, CEA > 5 ng/ml and SUVR showed an AUC of 0.899 (sensitivity = 0.909, specificity = 0.735, PPV = 0.899, NPV = 0.758). The diagnostic efficiency of the model was improved significantly compared with SUVR. The clinical efficacy of the model was confirmed by DCA.

CONCLUSIONS

The multiparameter diagnostic model composed of ¹⁸F-FDG PET metabolic parameters (SUVR) and clinical variables, including patient signs (fever), medical history (cholecystolithiasis) and laboratory examination (CEA > 5 ng/ml), has good diagnostic efficacy in the differential diagnosis of nonmetastatic GBC and cholecystitis.

Multiparameter diagnostic model based on 18F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas

OBJECTIVE

To evaluate the diagnostic performance of combined multiparametric ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸FDG PET) with clinical characteristics in differentiating thymic epithelial tumors (TETs) from thymic lymphomas.

PATIENTS AND METHODS

A total of 173 patients with 80 TETs and 93 thymic lymphomas who underwent ¹⁸F-FDG PET/CT before treatment were enrolled in this retrospective study. All patients were confirmed by pathology, and baseline characteristics and clinical data were also collected. The semi-parameters of ¹⁸F-FDG PET/CT, including lesion size, SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), TLG (total lesion glycolysis), MTV (metabolic tumor volume) and SUVR (tumor-to-normal liver standard uptake value ratio) were evaluated. The differential diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. Integrated discriminatory improvement (IDI) and net reclassification improvement (NRI), and Delong test were used to evaluate the improvement in diagnostic efficacy. The clinical efficacy was evaluated by decision curve analysis (DCA).

RESULTS

Age, clinical symptoms, and metabolic parameters differed significantly between patients with TETs and thymic lymphomas. The ROC curve analysis of SUVR showed the highest differentiating diagnostic value (sensitivity = 0.763; specificity = 0.888; area under the curve [AUC] = 0.881). The combined diagnostics model of age, clinical symptoms and SUVR resulted in the highest AUC of 0.964 (sensitivity = 0.882, specificity = 0.963). Compared with SUVR, the diagnostic efficiency of the model was improved significantly. The DCA also confirmed the clinical efficacy of the model.

CONCLUSIONS

The multiparameter diagnosis model based on ¹⁸F-FDG PET and clinical characteristics had excellent value in the differential diagnosis of TETs and thymic lymphomas.

Salivary gland carcinoma in children and adolescents: The EXPeRT/PARTNER diagnosis and treatment recommendations

Salivary gland carcinomas (SGCs) are rare during childhood and adolescence. Consequently, no standardized recommendations for the diagnosis and therapeutic management of pediatric SGC are available, and pediatric oncologists and surgeons generally follow adult guidelines. Complete surgical resection with adequate margins constitutes the cornerstone of treatment. However, the indications and modalities of adjuvant therapy remain controversial and may be challenging in view of the potential long-term toxicities in the pediatric population. This paper presents the consensus recommendations for the diagnosis and treatment of children and adolescents with SGCs, established by the European Cooperative Study Group for Pediatric Rare Tumors (EXPeRT) within the EU-funded PARTNER project (Paediatric Rare Tumours Network - European Registry).

Diagnostic and Prognostic Utility of 18F-FDG PET/CT in Recurrent Salivary Gland Cancers

OBJECTIVE. The role of ¹⁸F-FDG PET/CT in the evaluation of recurrent salivary gland tumors remains poorly defined. We investigated the diagnostic and prognostic utility of PET in this setting. MATERIALS AND METHODS. A total of 146 patients with recurrent salivary gland cancer were treated at our institution between January 2002 and December 2015. Patients who underwent FDG PET/CT and conventional imaging (CT or MRI) within 3 months of recurrence (n = 78) were included in this retrospective analysis. On FDG PET/CT, we measured the SUV_max, total body metabolic tumor volume of all lesions, and total lesion glycolysis of all lesions to determine the intensity and extent of FDG-avid disease. We assessed the correlation of FDG PET/CT findings with clinicopathologic features, progression-free survival, and overall survival. RESULTS. FDG PET/CT was positive for recurrence in 74 of 78 patients (94.9%) and falsely negative in four patients (5.1%). In comparison with conventional imaging, FDG PET/CT performed for restaging detected additional recurrent lesions in 14 patients (17.9%). The median SUV_max was 7.4, the median total body metabolic tumor volume was 30.1 cm³, and median total lesion glycolysis was 97.3 g/mL × cm³. Sixty-six patients had progressive disease, and 54 died. Univariate and multivariate Cox hazards analysis identified pathologic risk group (p = .04), total body metabolic tumor volume (p < .001), and total lesion glycolysis (p < .001) as independent prognostic factors for progression-free survival and identified age (p = .05), total body metabolic tumor volume (p < .001), and total lesion glycolysis (p < .001) as independent prognostic factors for overall survival. CONCLUSION. In patients with recurrent salivary gland cancer, FDG PET/CT is useful as a single test for defining the extent of disease and providing prognostic information, which may help in selecting appropriate treatment strategies.

Development and validation of a prognostic model incorporating [18F]FDG PET/CT radiomics for patients with minor salivary gland carcinoma

OBJECTIVES

The aim of this study was to develop and validate a prognostic model incorporating [¹⁸F]FDG PET/CT radiomics for patients of minor salivary gland carcinoma (MSGC).

METHODS

We retrospectively reviewed the pretreatment [¹⁸F]FDG PET/CT images of 75 MSGC patients treated with curative intent. Using a 1.5:1 ratio, the patients were randomly divided into a training and validation group. The main outcome measurements were overall survival (OS) and relapse-free survival (RFS). All of the patients were followed up for at least 30 months or until death. Following segmentation of tumors and lymph nodes on PET images, radiomic features were extracted. The prognostic significance of PET radiomics and clinical parameters in the training group was examined using receiver operating characteristic curve analysis. Variables showing a significant impact on OS and RFS were entered into multivariable Cox regression models. Recursive partitioning analysis was subsequently implemented to devise a prognostic index, whose performance was examined in the validation group. Finally, the performance of the index was compared with clinical variables in the entire cohort and nomograms for surgically treated cases.

RESULTS

The training and validation groups consisted of 45 and 30 patients, respectively. The median follow-up time in the entire cohort was 59.5 months. Eighteen relapse, 19 dead, and thirteen relapse, eight dead events were found in the training and validation cohorts, respectively. In the training group, two factors were identified as independently associated with poor OS, i.e., (1) tumors with both high maximum standardized uptake value (SUV_max) and discretized intensity entropy and (2) poor performance status or N2c-N3 stage. A prognostic model based on the above factors was devised and showed significant higher concordance index (C-index) for OS than those of AJCC stage and high-risk histology (C-index: 0.83 vs. 0.65, P = 0.005; 0.83 vs. 0.54, P < 0.001, respectively). This index also demonstrated superior performance than nomogram for OS (C-index: 0.88 vs. 0.70, P = 0.017) and that for RFS (C-index: 0.87 vs. 0.72, P = 0.004).

CONCLUSIONS

We devised a novel prognostic model that incorporates [¹⁸F]FDG PET/CT radiomics and may help refine outcome prediction in patients with MSGC.

Primary tumor standardized uptake value (SUVmax) measured on 18F-FDG PET/CT and mixed NSCLC components predict survival in surgical-resected combined small-cell lung cancer

Purpose

The combined small-cell lung cancer (c-SCLC) is rare and has unique clinicopathological futures. The aim of this study is to investigate ¹⁸F-FDG PET/CT parameters and clinicopathological factors that influence the prognosis of c-SCLC.

Methods

Between November 2005 and October 2014, surgical-resected tumor samples from c-SCLC patients who received preoperative ¹⁸F-FDG PET/CT examination were retrospectively reviewed. The maximum standardized uptake value (SUV_max), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were used to evaluate metabolic parameters in primary tumors. The survivals were evaluated with the Kaplan–Meier method. Univariate and multivariate analyses were used to evaluate potential prognostic factors.

Results

Thirty-one patients were enrolled, with a median age of 62 (range: 35 − 79) years. The most common mixed component was squamous cell carcinoma (SCC, n = 12), followed by large-cell carcinoma (LCC, n = 7), adenocarcinoma (AC, n = 6), spindle cell carcinoma (n = 4), adenosquamous carcinoma (n = 1) and atypical carcinoid (n = 1). The median follow-up period was 53.0 (11.0–142.0) months; the 5-year overall survival (OS) and progression-free survival(PFS) rate were 48.4% and 35.5%, respectively. Univariate survival analysis showed that gender, smoking history, tumor location were associated with PFS (P = 0.036, P = 0.043, P = 0.048), SUVmax and TNM stage were closely related to PFS in both Mixed SCC and non-SCC component groups (P = 0.007, P = 0.048). SUV_max, smoking history, tumor size and mixed SCC component were influencing factors of OS in patients (P = 0.040, P = 0.041, P = 0.046, P = 0.029). Multivariate survival analysis confirmed that TNM stage (HR = 2.885, 95%CI: 1.323–6.289, P = 0.008) was the most significantly influential factor for PFS. High SUV_max value (HR = 9.338, 95%CI: 2.426–35.938, P = 0.001) and mixed SCC component (HR = 0.155, 95%CI: 0.045–0.530, P = 0.003) were poor predictors for OS.

Conclusion

Surgical-resected c-SCLCs have a relatively good prognosis. TNM stage is the most significant factor influencing disease progression in surgical-resected c-SCLCs. SUVmax and mixed NSCLC components within c-SCLCs had a considerable influence on the survival. Both high SUVmax and mixed SCC component are poor predictors for patients with c-SCLCs.