Differentiation between pancreatic metastases from clear cell renal cell carcinoma and pancreatic neuroendocrine tumor using double-echo chemical shift imaging

Navigating advanced renal cell carcinoma in the era of artificial intelligence

BACKGROUND

Research has helped to better understand renal cell carcinoma and enhance management of patients with locally advanced and metastatic disease. More recently, artificial intelligence has emerged as a powerful tool in cancer research, particularly in oncologic imaging. BODY: Despite promising results of artificial intelligence in renal cell carcinoma research, most investigations have focused on localized disease, while relatively fewer studies have targeted advanced and metastatic disease. This paper summarizes major artificial intelligence advances focusing mostly on their potential clinical value from initial staging and identification of high-risk features to predicting response to treatment in advanced renal cell carcinoma, while addressing major limitations in the development of some models and highlighting new avenues for future research.

CONCLUSION

Artificial intelligence-enabled models have a great potential in improving clinical practice in the diagnosis and management of advanced renal cell carcinoma, particularly when developed from both clinicopathologic and radiologic data.

Differentiation between renal cell carcinoma metastases to the pancreas and pancreatic neuroendocrine tumors in patients with renal cell carcinoma on CT or MRI

PURPOSE

To determine whether renal cell carcinoma metastases (RCC-Mets) to the pancreas can be differentiated from pancreatic neuroendocrine tumors (PNETs) in patients with RCC on CT or MRI at presentation.

METHODS

This retrospective study included patients with biopsy-proven RCC-Mets (n = 102) or PNETs (n = 32) at diagnosis or after nephrectomy for RCC. Inter-observer agreement (Cohen kappa) was assessed in 95 patients with independent reads by two radiologists, with discrepancies resolved by consensus for final analysis. The remaining 39 cases underwent consensus reads by two different radiologists for final analysis. The CT/MRI images were reviewed for number, size, regional distribution, parenchymal location (exophytic or intrapancreatic), contrast-enhancement, and enhancement pattern of pancreatic lesions in the available phases. Statistical tests were conducted using two sample t-tests and Pearson's chi-squared test for numeric and categorical variables respectively.

RESULTS

The study group comprised of 134 patients (90 males) with 265 lesions (229 RCC-Mets and 36 PNETs). Patients with PNETs were significantly younger (62 ± 12 years vs. 67 ± 9 years, p = 0.013). Inter-observer agreement for CT/MRI features was excellent across multiple imaging variables (k = 0.86-1.00). Most PNETs were single lesions (88 vs. 63%, p = 0.008), smaller in size (14 mm vs. 23 mm, p = 0.042), more common in the body and tail (81 vs. 57%, p = 0.01), showed homogeneous contrast enhancement (64-79% vs. 39-49%, p < 0.01-0.03), less T1-hypointense (80 vs. 99%, p = 0.002) and more DWI hyperintense (71 vs. 58%, p < 0.001) compared to RCC-Mets.

CONCLUSION

PNETs are typically single, occur in distal pancreas, and enhance homogeneously compared to RCC-Mets which are often multiple, occur in the proximal pancreas, and enhance heterogeneously.

Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

Background

Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.

Methods

This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.

Results

Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

Conclusions

The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.

Diagnostic performance of MRI, SPECT, and PET in detecting renal cell carcinoma: a systematic review and meta-analysis

Background

Renal cell carcinoma (RCC) is one of the most common malignancies worldwide. Noninvasive imaging techniques, such as magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET), have been involved in increasing evolution to detect RCC. This meta-analysis aims to compare to compare the performance of MRI, SPECT, and PET in the detection of RCC in humans, and to provide evidence for decision-making in terms of further research and clinical settings.

Methods

Electronic databases including PubMed, Web of Science, Embase, and Cochrane Library were systemically searched. The keywords such as “magnetic resonance imaging”, “MRI”, “single-photon emission computed tomography”, “SPECT”, “positron emission tomography”, “PET”, “renal cell carcinoma” were used for the search. Studies concerning MRI, SPECT, and PET for the detection of RCC were included. Pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve (AUC), etc. were calculated.

Results

A total of 44 articles were finally detected for inclusion in this study. The pooled sensitivities of MRI, ¹⁸F-FDG PET and ¹⁸F-FDG PET/CT were 0.80, 0.83, and 0.89, respectively. Their respective overall specificities were 0.90, 0.86, and 0.88. The pooled sensitivity and specificity of MRI studies at 1.5 T were 0.86 and 0.94, respectively. With respect to prospective PET studies, the pooled sensitivity, specificity and AUC were 0.90, 0.93 and 0.97, respectively. In the detection of primary RCC, PET studies manifested a pooled sensitivity, specificity, and AUC of 0.77, 0.80, and 0.84, respectively. The pooled sensitivity, specificity, and AUC of PET/CT studies in detecting primary RCC were 0.80, 0.85, and 0.89.

Conclusion

Our study manifests that MRI and PET/CT present better diagnostic value for the detection of RCC in comparison with PET. MRI is superior in the diagnosis of primary RCC.

Differentiation between pancreatic metastases from renal cell carcinoma and pancreatic neuroendocrine neoplasm using endoscopic ultrasound

OBJECTIVES

Pancreatic metastases from renal cell carcinoma (PRCC) often appear many years after treatment of the primary tumor, and differentiation from pancreatic neuroendocrine neoplasm (PanNEN) can be challenging due to their hypervascularity. Here, we investigated the utility of endoscopic ultrasound (EUS) for differentiation of these conditions.

METHODS

A retrospective analysis was performed in 17 and 79 consecutive patients with pathologically proven PRCC and non-functional PanNEN who were examined by EUS. In cases examined by EUS elastography or contrast-enhanced harmonic EUS (CH-EUS), the lesions were classified as stiff or soft, or into three vascular patterns as hypoechoic, isoechoic, and hyperechoic. CH-EUS images at 20 s, 40 s, 60 s, 3 min and 5 min were used for evaluation. EUS images were independently reviewed by two readers who were blinded to all clinical information.

RESULTS

The patients with PRCC were significantly older than those with PanNEN (median, 71 (range, 45-81) vs. 58 (22-76), P = 0.001) and more often had multiple tumors (6/17 (35%) vs. 7/79 (9%), P = 0.010). In EUS findings, PRCC lesions significantly more frequently had a marginal hypoechoic zone (MHZ) (11/17 (65%) vs. 27/79 (34%), P = 0.028), being classified as soft (12/13 (92%) vs. 26/58 (45%), P = 0.002), and showed sustained hyperechoic vascular patterns at 5 min (7/8 (88%) vs. 4/59 (7%), P < 0.001) compared to PanNEN lesions.

CONCLUSIONS

The presence of a MHZ, a soft lesion, and a sustained hyperechoic vascular pattern in EUS may be useful for differentiating PRCC from PanNEN.

MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma

OBJECTIVE

To evaluate the performance of a multiparametric MRI radiomics-based nomogram for the individualised prediction of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

Two-hundred and one patients (training cohort: n = 126; internal validation cohort: n = 39; external validation cohort: n = 36) with ccRCC were retrospectively enrolled between January 2013 and June 2019. In the training cohort, the optimal MRI radiomics features were selected and combined to calculate the radiomics score (Rad-score). Incorporating Rad-score and SDM-related clinicoradiologic characteristics, the radiomics-based nomogram was established by multivariable logistic regression analysis, then the performance of the nomogram (discrimination and clinical usefulness) was evaluated and validated subsequently. Moreover, the prediction efficacy for SDM in ccRCC subgroups of different sizes was also assessed.

RESULTS

Incorporating Rad-score derived from 9 optimal MR radiomics features (age, pseudocapsule and regional lymph node), the radiomics-based nomogram was capable of predicting SDM in the training cohort (area under the ROC curve (AUC) = 0.914) and validated in both the internal and external cohorts (AUC = 0.854 and 0.816, respectively) and also showed a convincing predictive power in ccRCC subgroups of different sizes (≤ 4 cm, AUC = 0.875; 4-7 cm, AUC = 0.891; 7-10 cm, 0.908; > 10 cm, AUC = 0.881). Decision curve analysis indicated that the radiomics-based nomogram is of clinical usefulness.

CONCLUSIONS

The multiparametric MRI radiomics-based nomogram could achieve precise individualised prediction of SDM in patients with ccRCC, potentially improving the management of ccRCC.

KEY POINTS

• Radiomics features derived from multiparametric magnetic resonance images showed relevant association with synchronous distant metastasis in clear cell renal cell carcinoma. • MRI radiomics-based nomogram may serve as a potential tool for the risk prediction of synchronous distant metastasis in clear cell renal cell carcinoma.

Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features

PURPOSE

To assess qualitative and quantitative imaging features on enhanced CT that may differentiate pancreatic neuroendocrine tumors (PNETs) from pancreatic renal cell carcinoma (RCC) metastases.

METHODS

This IRB-approved multi-center retrospective case-control study compared 43 resected PNETs and 28 resected RCC metastases with pre-operative enhanced CT identified consecutively between 2003 and 2017. Two blinded radiologists (R1/R2) independently assessed tumor location, attenuation (relative to pancreas), composition (solid/cystic/mixed), homogeneity (homogeneous/heterogeneous), calcification, multiplicity, and for main pancreatic duct (MPD) dilation. Tumors were segmented for quantitative texture analysis. Data were analyzed with Chi square, logistic regression, and receiver operating characteristic (ROC). Inter-observer agreement was assessed (Cohen's kappa).

RESULTS

There was no difference in age, gender, location, attenuation, or composition (P > 0.05) between groups. PNETs were larger than RCC metastases (37 ± 23 mm vs. 26 ± 21 mm, P = 0.038), more frequently solitary (P < 0.001), subjectively more heterogeneous (P = 0.033/0.144, R1/R2), and associated with calcification (P = 0.002/0.004) and MPD dilation (P = 0.025/0.006). Agreement for subjective features was moderate-to-almost perfect (K = 0.4879-0.9481). Quantitative texture analysis showed higher entropy in PNETs (6.32 ± 0.49 versus 5.96 ± 0.53; P = 0.004) with no difference in other features studied (P > 0.05). Entropy had ROC area under the curve for diagnosis of PNET of 0.77 ± 0.06, with optimal sensitivity/specificity of 71.4/79.1%.

CONCLUSIONS

Compared to pancreatic RCC metastases, PNETs are larger, more frequently solitary, contain calcification, show MPD dilation, and are subjectively and quantitatively more heterogeneous tumors.