The correlation between molecular pathological profiles and metabolic parameters of 18F-FDG PET/CT in patients with gastroesophageal junction cancer

Hypermetabolic Esophagitis Dessicans Superficialis Mimicking Esophageal Cancer on FDG PET/CT

ABSTRACT

An 83-year-old man with history of multiple myeloma obtained an FDG PET/CT for surveillance. Although there was no evidence of metabolically active myeloma, an incidental highly FDG-avid focus was noted at the gastroesophageal junction. Findings were interpreted as malignancy. The patient underwent esophagogastroduodenoscopy, which showed nonerythematous desquamation of the distal esophagus consistent with esophagitis dessicans superficialis (EDS) without any evidence of malignancy. There is paucity of literature regarding EDS and its image findings. This case illustrates an instance of focal mass-like uptake on FDG PET/CT, which represents an endoscopy- and pathology-confirmed EDS and mimics esophageal cancer.

Metabolic activity via 18F-FDG PET/CT is predictive of microsatellite instability status in colorectal cancer

Purpose

Identification of microsatellite instability high (MSI-H) colorectal cancer (CRC) is crucial for screening patients most likely to benefit from immunotherapy. We aim to investigate whether the metabolic characteristics is related to MSI status and can be used to predict the MSI-H CRC.

Methods

A retrospective analysis was conducted on 420 CRC patients who were identified via [¹⁸F]fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography(CT) prior to therapy. Maximum standardized uptake (SUV_max), mean standardized uptake (SUV_mean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary tumor were calculated and compared between MSI-H and microsatellite stability (MSS). Predictive factors of MSI status were selected from metabolic parameters and clinicopathological profiles via a multivariate analysis.

Results

Of 420 colorectal cancers, 44 exhibited a high incidence of MSI. Both MTV and TLG were significantly higher in MSI-H group compared with the MSS group (P = 0.004 and P = 0.010, respectively). Logistic regression analysis indicated that CRC with MSI-H were related to younger age (P = 0.013), primary lesion located at right hemi-colon (P < 0.001) and larger MTV on PET/CT imaging (P = 0.019). MTV more than 32.19 of colorectal cancer was linked to the presence of MSI (P = 0.019).

Conclusion

Tumor metabolic burden were higher in MSI-H CRC which may be useful for predicting the MSI status of CRC patient and thus aid in determination of immunotherapy for patients with CRC.

FDG-PET/CT in predicting aggressiveness of rectal cancer

Background

Treatment response varies significantly among rectal cancer patients. Tumor can show complete regression, stationary appearance, or even tumour progression during the treatment. It is also widely known that the rate of local recurrence is variable. Precise risk stratification of tumor aggressiveness is required for better per patient tailored treatment plan and predicting the overall prognosis of rectal cancer patients The aim of this study was to assess different parameters of baseline [18F] fluorodeoxyglucose positron emission tomography/computed tomography [(18F) FDG-PET/CT] as a non-invasive tool in predicting aggressiveness of the rectal cancer.

Results

Overall, 33 patients were included [19 moderately differentiated adenocarcinoma, 10 poorly differentiated adenocarcinoma and 4 mucinous adenocarcinomas (MAC)]. SUV estimates (SUV max, SUV mean) were greater in the moderately adenocarcinoma group (p = 0.003 and p = 0.019, respectively). MTV and TLG values were similar between the three histopathological groups (p = 0.763 and p = 0.701, respectively). There was no correlation between SUVmax of primary tumor and MTV (r = 0.034; p = 0.849). However, SUVmax and TLG were significantly correlated (r = 0.517; p = 0.002). Strong correlation between tumor size and MTV (r = 0.489; p = 0.003), and TLG (r = 0.506; p = 0.003) were observed. No significant association was found between MTV and TLG and the clinical stage of rectal cancer.

Conclusion

Baseline 18F-FDG PET/CT parameters cannot be used alone as a non-invasive diagnostic technique in assessing aggressiveness and prognosis in patients with primary rectal cancer, and further clinical studies are needed before considering the prognostic role of FDG-PET/CT in rectal cancer.

The Value of Predicting Human Epidermal Growth Factor Receptor 2 Status in Adenocarcinoma of the Esophagogastric Junction on CT-Based Radiomics Nomogram

Purpose

We developed and validated a CT-based radiomics nomogram to predict HER2 status in patients with adenocarcinoma of esophagogastric junction (AEG).

Method

A total of 101 patients with HER2-positive (n=46) and HER2-negative (n=55) esophagogastric junction adenocarcinoma (AEG) were retrospectively analyzed. They were then randomly divided into a training cohort (n=70) and a verification cohort (n=31). The radiomics features were obtained from the portal phase of the CT enhanced scan. We used the least absolute shrinkage and selection operator (LASSO) logistic regression method to select the best radiomics features in the training cohort, combined them linearly, and used the radiomics signature formula to calculate the radiomics score (Rad-score) of each AEG patient. A multivariable logistic regression method was applied to develop a prediction model that incorporated the radiomics signature and independent risk predictors. The prediction performance of the nomogram was evaluated using the training and validation cohorts.

Result

In the training (P<0.001) and verification groups (P<0.001), the radiomics signature combined with seven radiomics features was significantly correlated with HER2 status. The nomogram composed of CT-reported T stage and radiomics signature showed very good predictive performance for HER2 status. The area under the curve (AUC) of the training cohort was 0.946 (95% CI: 0.919–0.973), and that of the validation group was 0.903 (95% CI: 0.847–0.959). The calibration curve of the radiomics nomogram showed a good degree of calibration. Decision-curve analysis revealed that the radiomics nomogram was useful.

Conclusion

The nomogram CT-based radiomics signature combined with CT-reported T stage can better predict the HER2 status of AEG before surgery. It can be used as a non-invasive prediction tool for HER2 status and is expected to guide clinical treatment decisions in clinical practice, and it can assist in the formulation of individualized treatment plans.

The value of multiparameter 18F-FDG PET/CT imaging in differentiating retroperitoneal paragangliomas from unicentric Castleman disease

The aim of this study was to investigate the value of multiple parameters retrieved from the FDG PET/CT studies, including SUVmax, SUVmean, SUVpeak, MTV, TLG, tumour size in differentiating retroperitoneal paragangliomas from UCD. 28 patients with solitary retroperitoneal masses who had undergone preoperative ¹⁸F-FDG PET/CT were retrospectively evaluated. Histopathology by surgical resection confirmed 17 paragangliomas and 11 UCDs. SUVmax, SUVmean, SUVpeak, MTV, TLG, and tumour size of ¹⁸F-FDG PET/CT were measured for each patient. Mann–Whitney U-test was used to assess differences in multiple parameters between paragangliomas and UCDs. The ROC curve analysis was performed to determine the differential diagnostic value of these parameters. Paragangliomas presented significantly higher SUVmax (P < 0.001), SUVmean (P = 0.001), SUVpeak (P < 0.001), and TLG (P = 0.024) than UCDs, whereas no significant difference was observed in MTV. The AUCs for differentiating paragangliomas and UCDs were 0.920, 0.888, 0.909, and 0.765 for SUVmax, SUVmean, SUVpeak, and TLG, respectively. The SUVmax cut-off of 7.75 yielded 82.4% sensitivity and 100% specificity for predicting paragangliomas. This study indicated that ¹⁸F FDG PET/CT-derived multiple metabolic parameters are useful in distinguishing between paragangliomas and UCDs. SUVmax showed the best result for the differential diagnosis of these two diseases among multiple metabolic parameters.