Semi-quantitative F-18-FDG PET/computed tomography parameters for prediction of grade in patients with renal cell carcinoma and the incremental value of diuretics

Can 18 F FDG PET/CT metabolic parameters be used to noninvasively differentiate between different histopathological subtypes and Fuhrman grades of renal cell cancer?

AIM

To evaluate relationship between metabolic PET metabolic parameters and size of the primary tumor, various histopathological subtypes of renal cell carcinoma (RCC) and Fuhrman grade of the tumors.

MATERIAL AND METHODS

Retrospective analysis of 93 biopsy-proven RCC patients who underwent pretreatment flourine 18 flourodeoxyglucose PET/computed tomography ( 18 F FDG PET/CT) was performed. Quantitative PET parameters, size of the primary tumor, histopathological subtypes and Fuhrman grades of the tumor were extracted. We tried to assess if there was any significant difference in the metabolic patterns of various histopathological subtypes of RCCs, Fuhrman grade of the tumors and size of the primary tumor.

RESULTS

A significant correlation was noted between the size of primary tumor and maximum standardized uptake value (SUV max ), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) ( P  < 0.01, P  < 0.001 and P  < 0.001, respectively). SUV max values correlated significantly with the histopathological subtype ( P  < 0.001). Further sub-analyses was also done by segregating the patients into Low grade (Fuhrman grade 1 and 2) vs. High grade (Fuhrman grade 3 and 4). SUV max , MTV and TLG were significantly different between high grade vs. low grade tumors. ROC analysis yielded cut off values for SUV max , MTV and TLG to differentiate between high grade from low grade tumors.

CONCLUSION

FDG PET/CT with the use of metabolic PET parameters can differentiate between different histopathological subtypes of RCC. Incorporation of metabolic parameters into clinical practice can potentially noninvasively identify patients with low-grade vs. high-grade RCC.

Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature

Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as ¹⁸F-fluorodeoxyglucose (FDG), ¹²⁴I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and ¹¹C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, ⁹⁹Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.

Molecular Imaging of Renal Cell Carcinoma in Precision Medicine

Renal cell carcinoma (RCC) is the sixth most common cancer among men and the ninth among women, and its prognosis is closely correlated with metastasis. Targeted therapy and immunotherapy are the main adjuvant treatments for advanced RCC and require early diagnosis, precise assessment, and prediction of the therapeutic responses. Current conventional imaging methods of RCC only provide structural information rather than biological processes. Noninvasive diagnostic tools are therefore needed to image RCC early and accurately at the molecular level. Nuclear medicine imaging combines the high sensitivity of radionuclides with the high resolution of structural imaging to visualize the metabolic processes and specific targets of RCC for more accurate and reliable diagnosis, staging, prognosis prediction, and response assessment. This review summarizes the most recent applications of nuclear medicine receptor imaging and metabolic imaging in RCC and highlights future development perspectives in the field.

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management.

OBJECTIVE

To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization.

EVIDENCE ACQUISITION

The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr.

EVIDENCE SYNTHESIS

Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice.

CONCLUSIONS

A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis.

PATIENT SUMMARY

Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.

Metabolic tumour volume on 18F-FDG PET/CT predicts extended pathological T stages in patients with renal cell carcinoma at staging

We evaluated the predictive value of ¹⁸F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/CT (PET/CT) for extended pathological T (pT) stages (≥ pT3a) in Renal cell carcinoma (RCC) patients at staging. Thirty-eight RCC patients who underwent ¹⁸F-FDG PET/CT at staging, followed by radical nephrectomy between September 2016 and September 2018, were included in this prospective study. Patients were classified into two groups (limited pT stage: stage T1/2, n = 17; extended pT stage: T3/4, n = 21). Univariate and multivariate logistic regression analyses were performed to identify clinicopathological and metabolic variables to predict extended pT stages. ¹⁸F-FDG metabolic parameters were compared in relation to International Society of Urological Pathology (ISUP) grade and lymphovascular invasion (LVI). In univariate analysis, maximum standardised uptake value, metabolic tumour volume (MTV), and ISUP grade were significant. In multivariate analysis, MTV was the only significant factor of extended pT stages. With a cut-off MTV of 21.2, an area under the curve was 0.944, which was higher than 0.824 for clinical T stages (p = 0.037). In addition, high MTV, but not tumour size, was significantly correlated with aggressive pathologic features (ISUP grade and LVI). High glycolytic tumour volume on ¹⁸F-FDG PET/CT in RCC patients at staging is predictive of extended pT stages which could aid decision-making regarding the best type of surgery.

18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma

PURPOSE

This article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery.

METHODS

A retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified.

RESULTS

In the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively.

CONCLUSION

PET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade.

2-[18F]FDG PET/CT parameters associated with WHO/ISUP grade in clear cell renal cell carcinoma

PURPOSE

To explore the potential parameters from preoperative 2-[¹⁸F]FDG PET/CT that might associate with the World Health Organization/the International Society of Urological Pathology (WHO/ISUP) grade in clear cell renal cell carcinoma (ccRCC).

METHODS

One hundred twenty-five patients with newly diagnosed ccRCC who underwent 2-[¹⁸F]FDG PET/CT prior to surgery or biopsy were retrospectively reviewed. The metabolic parameters and imaging features obtained from 2-[¹⁸F]FDG PET/CT examinations were analyzed in combination with clinical characteristics. Univariate and multivariate logistic regression analyses were performed to identify the predictive factors of WHO/ISUP grade.

RESULTS

Metabolic parameters of primary tumor maximum standardized uptake value (SUVmax), tumor-to-liver SUV ratio (TLR), and tumor-to-kidney SUV ratio (TKR) were significantly different between any two of the four different WHO/ISUP grades, except those between the WHO/ISUP grade 3 and grade 4. The optimal cutoff values to predict high WHO/ISUP grade for SUVmax, TLR, and TKR were 4.15, 1.63, and 1.59, respectively. TLR (AUC: 0.841) was superior to TKR (AUC: 0.810) in distinguishing high and low WHO/ISUP grades (P = 0.0042). In univariate analysis, SUVmax, TLR, TKR, primary tumor size, tumor thrombus, distant metastases, and clinical symptoms could discriminate between the high and low WHO/ISUP grades (P < 0.05). In multivariate analysis, TLR (P < 0.001; OR: 1.732; 95%CI: 1.289-2.328) and tumor thrombus (P < 0.001; OR: 6.199; 95%CI: 2.499-15.375) were significant factors for differentiating WHO/ISUP grades.

CONCLUSION

Elevated TLR (> 1.63) and presence of tumor thrombus from preoperative 2-[¹⁸F]FDG PET/CT can distinguish high WHO/ISUP grade ccRCC effectively. 2-[¹⁸F]FDG PET/CT may be a feasible method for noninvasive assessment of WHO/ISUP grade.

The effects of baicalin on piglets challenged with Glaesserella parasuis

Glaesserella parasuis (G. parasuis) causes porcine vascular inflammation and damage. Baicalin is reported to have antioxidant and anti-inflammatory functions. However, whether baicalin protects piglets against G. parasuis challenge and the potential protective mechanism have not been investigated. Therefore, in this study, we comprehensively examined the protective efficacy of baicalin in piglets challenged with G. parasuis and the possible protective mechanism. Our results show that baicalin attenuated the release of the inflammation-related cytokines interleukin (IL) 1β, IL6, IL8, IL10, and tumour necrosis factor α (TNF-α) and reduced high mobility group box 1 (HMGB1) production and cell apoptosis in piglets infected with G. parasuis. Baicalin also inhibited the activation of the mitogen-activated protein kinase (MAPK) signalling pathway and protected piglets against G. parasuis challenge. Taken together, our data suggest that baicalin could protect piglets from G. parasuis by reducing HMGB1 release, attenuating cell apoptosis, and inhibiting MAPK signalling activation, thereby alleviating the inflammatory response induced by the bacteria. Our results suggest that baicalin has utility as a novel therapeutic drug to control G. parasuis infection.