Kidney tumor diffusion-weighted magnetic resonance imaging derived ADC histogram parameters combined with patient characteristics and tumor volume to discriminate oncocytoma from renal cell carcinoma

PURPOSE

To assess the ability to discriminate oncocytoma from RCC based on a model using whole tumor ADC histogram parameters with additional use of tumor volume and patient characteristics.

METHOD

In this prospective study, 39 patients (mean age 65 years, range 28-79; 9/39 (23%) female) with 39 renal tumors (32/39 (82%) RCC and 7/39 (18%) oncocytoma) underwent multiparametric MRI between November 2014 and June 2018. Two regions of interest (ROIs) were drawn to cover both the entire tumor volume and a part of healthy renal cortex. ROI ADC maps were calculated using a mono-exponential model and ADC histogram distribution parameters were calculated. A logistic regression model was created using ADC histogram parameters, radiographic and patient characteristics that were significantly different between oncocytoma and RCC. A ROC curve of the model was constructed and the AUC, sensitivity and specificity were calculated. Furthermore, differences in intra-patient ADC histogram parameters between renal tumor and healthy cortex were calculated. A separate ROC curve was constructed to differentiate oncocytoma from RCC using statistically significant intra-patient parameter differences.

RESULTS

ADC standard deviation (p = 0.008), entropy (p = 0.010), tumor volume (p = 0.012), and patient sex (p = 0.018) were significantly different between RCC and oncocytoma. The regression model of these parameters combined had an ROC-AUC of 0.91 with a sensitivity of 86% and specificity of 84%. Intra-patient difference in ADC 25th percentile (p < 0.01) and entropy (p = 0.030) combined had a ROC-AUC of 0.86 with a sensitivity and specificity of 86%, and 81%, respectively.

CONCLUSION

A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma. Although less accurate, intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used. Although the results of this preliminary study do not yet justify clinical use of the model, it does stimulate further research using whole tumor ADC histogram parameters.

The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment

Objectives

Compare total tumor volume (TTV) response after systemic treatment to Response Evaluation Criteria in Solid Tumors (RECIST1.1) and assess the prognostic value of TTV change and RECIST1.1 for recurrence-free survival (RFS) in patients with colorectal liver-only metastases (CRLM).

Background

RECIST1.1 provides unidimensional criteria to evaluate tumor response to systemic therapy. Those criteria are accepted worldwide but are limited by interobserver variability and ignore potentially valuable information about TTV.

Methods

Patients with initially unresectable CRLM receiving systemic treatment from the randomized, controlled CAIRO5 trial (NCT02162563) were included. TTV response was assessed using software specifically developed together with SAS analytics. Baseline and follow-up computed tomography (CT) scans were used to calculate RECIST1.1 and TTV response to systemic therapy. Different thresholds (10%, 20%, 40%) were used to define response of TTV as no standard currently exists. RFS was assessed in a subgroup of patients with secondarily resectable CRLM after induction treatment.

Results

A total of 420 CT scans comprising 7820 CRLM in 210 patients were evaluated. In 30% to 50% (depending on chosen TTV threshold) of patients, discordance was observed between RECIST1.1 and TTV change. A TTV decrease of >40% was observed in 47 (22%) patients who had stable disease according to RECIST1.1. In 118 patients with secondarily resectable CRLM, RFS was shorter for patients with less than 10% TTV decrease compared with patients with more than 10% TTV decrease (P = 0.015), while RECIST1.1 was not prognostic (P = 0.821).

Conclusions

TTV response assessment shows prognostic potential in the evaluation of systemic therapy response in patients with CRLM.

Histopathological Growth Patterns and Survival After Resection of Colorectal Liver Metastasis: An External Validation Study

Background

After resection of colorectal cancer liver metastases (CRLM), 2 main histopathological growth patterns can be observed: a desmoplastic and a nondesmoplastic subtype. The desmoplastic subtype has been associated with superior survival. These findings require external validation.

Methods

An international multicenter retrospective cohort study was conducted in patients treated surgically for CRLM at 3 tertiary hospitals in the United States and the Netherlands. Determination of histopathological growth patterns was performed on hematoxylin and eosin-stained sections of resected CRLM according to international guidelines. Patients displaying a desmoplastic histopathological phenotype (only desmoplastic growth observed) were compared with patients with a nondesmoplastic phenotype (any nondesmoplastic growth observed). Cutoff analyses on the extent of nondesmoplastic growth were performed. Overall survival (OS) and disease-free survival (DFS) were estimated using Kaplan-Meier and multivariable Cox analysis. All statistical tests were 2-sided.

Results

In total 780 patients were eligible. A desmoplastic phenotype was observed in 19.1% and was associated with microsatellite instability (14.6% vs 3.6%, P = .01). Desmoplastic patients had superior 5-year OS (73.4%, 95% confidence interval [CI] = 64.1% to 84.0% vs 44.2%, 95% CI = 38.9% to 50.2%, P < .001) and DFS (32.0%, 95% CI = 22.9% to 44.7% vs 14.7%, 95% CI = 11.7% to 18.6%, P < .001) compared with their nondesmoplastic counterparts. A desmoplastic phenotype was associated with an adjusted hazard ratio for death of 0.36 (95% CI = 0.23 to 0.58) and 0.50 (95% CI = 0.37 to 0.66) for cancer recurrence. Prognosis was independent of KRAS and BRAF status. The cutoff analyses found no prognostic relationship between either OS or DFS and the extent of nondesmoplastic growth observed (all P > .1).

Conclusions

This external validation study confirms the remarkably good prognosis after surgery for CRLM in patients with a desmoplastic phenotype. The extent of nondesmoplastic growth does not affect prognosis.

Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

Objectives

To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).

Methods

Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests.

Results

For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.

Conclusions

With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.

Key Points

• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.

• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.

• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07668-x.

Accuracy of the neurosurgeons estimation of extent of resection in glioblastoma

BACKGROUND

The surgeons' estimate of the extent of resection (EOR) shows little accuracy in previous literature. Considering the developments in surgical techniques of glioblastoma (GBM) treatment, we hypothesize an improvement in this estimation. This study aims to compare the EOR estimated by the neurosurgeon with the EOR determined using volumetric analysis on the post-operative MR scan.

METHODS

Pre- and post-operative tumor volumes were calculated through semi-automatic volumetric assessment by three observers. Interobserver agreement was measured using intraclass correlation coefficient (ICC). A univariate general linear model was used to study the factors influencing the accuracy of estimation of resection percentage.

RESULTS

ICC was high for all three measurements: pre-operative tumor volume was 0.980 (0.969-0.987), post-operative tumor volume 0.974 (0.961-0.984), and EOR 0.947 (0.917-0.967). Estimation of EOR by the surgeon showed moderate accuracy and agreement. Multivariable analysis showed a statistically significant effect of operating neurosurgeon (p = 0.01), use of fluorescence (p < 0.001), and resection percentage (p < 0.001) on the accuracy of the EOR estimation.

CONCLUSION

All measurements through semi-automatic volumetric analysis show a high interobserver agreement, suggesting this to be a reliable assessment of EOR. We found a moderate reliability of the surgeons' estimate of EOR. Therefore, (early) post-operative MRI scanning for evaluation of EOR remains paramount.