The De Ritis and Neutrophil-to-Lymphocyte Ratios May Aid in the Risk Assessment of Patients with Metastatic Renal Cell Carcinoma

Selection and evaluation of preoperative systemic inflammatory response biomarkers model prior to cytoreductive nephrectomy using a machine-learning approach

INTRODUCTION

This study aimed to determine the prognostic value of a panel of SIR-biomarkers, relative to standard clinicopathological variables, to improve mRCC patient selection for cytoreductive nephrectomy (CN).

MATERIAL AND METHODS

A panel of preoperative SIR-biomarkers, including the albumin-globulin ratio (AGR), De Ritis ratio (DRR), and systemic immune-inflammation index (SII), was assessed in 613 patients treated with CN for mRCC. Patients were randomly divided into training and testing cohorts (65/35%). A machine learning-based variable selection approach (LASSO regression) was used for the fitting of the most informative, yet parsimonious multivariable models with respect to prognosis of cancer-specific survival (CSS). The discriminatory ability of the model was quantified using the C-index. After validation and calibration of the model, a nomogram was created, and decision curve analysis (DCA) was used to evaluate the clinical net benefit.

RESULTS

SIR-biomarkers were selected by the machine-learning process to be of high discriminatory power during the fitting of the model. Low AGR remained significantly associated with CSS in both training (HR 1.40, 95% CI 1.07-1.82, p = 0.01) and testing (HR 1.78, 95% CI 1.26-2.51, p = 0.01) cohorts. High levels of SII (HR 1.51, 95% CI 1.10-2.08, p = 0.01) and DRR (HR 1.41, 95% CI 1.01-1.96, p = 0.04) were associated with CSS only in the testing cohort. The exclusion of the SIR-biomarkers for the prognosis of CSS did not result in a significant decrease in C-index (- 0.9%) for the training cohort, while the exclusion of SIR-biomarkers led to a reduction in C-index in the testing cohort (- 5.8%). However, SIR-biomarkers only marginally increased the discriminatory ability of the respective model in comparison to the standard model.

CONCLUSION

Despite the high discriminatory ability during the fitting of the model with machine-learning approach, the panel of readily available blood-based SIR-biomarkers failed to add a clinical benefit beyond the standard model.

Potential Clinical Value of Pretreatment De Ritis Ratio as a Prognostic Biomarker for Renal Cell Carcinoma

Background

We performed this study to explore the prognostic value of the pretreatment aspartate transaminase to alanine transaminase (De Ritis) ratio in patients with renal cell carcinoma (RCC).

Methods

PubMed, EMBASE, Web of Science, and Cochrane Library were searched to identify all studies. The hazard ratio (HR) with a 95% confidence interval (CI) for overall survival (OS) and cancer-specific survival (CSS) were extracted to evaluate their correlation.

Results

A total of 6,528 patients from 11 studies were included in the pooled analysis. Patients with a higher pretreatment De Ritis ratio had worse OS (HR = 1.41, p < 0.001) and CSS (HR = 1.59, p < 0.001). Subgroup analysis according to ethnicity, disease stage, cutoff value, and sample size revealed that the De Ritis ratio had a significant prognostic value for OS and CSS in all subgroups.

Conclusions

The present study suggests that an elevated pretreatment De Ritis ratio is significantly correlated with worse survival in patients with RCC. The pretreatment De Ritis ratio may serve as a potential prognostic biomarker in patients with RCC, but further studies are warranted to support these results.

Albumin levels predict prognosis in advanced renal cell carcinoma treated with tyrosine kinase inhibitors: a systematic review and meta-analysis

PURPOSE

With the development of therapy and prognostic criteria for metastatic Renal Cell Carcinoma (mRCC), the prognostic value of serum albumin level has remained in dispute. The aim of this meta-analysis was to evaluate the role of pre-treatment albumin in predicting the prognosis of mRCC patients in the era of tyrosine kinase inhibitor (TKI) treatments.

METHODS

The qualitative and quantitative synthesis was conducted of studies retrieved from PubMed, Embase, and Cochrane library from inception of these databases to July 19, 2020. The hazard ratio (HR) and its 95% confidence interval (CI) of overall survival (OS) and progression-free survival (PFS) were extracted from studies comparing different levels of pre-treatment serum albumin (as a dichotomous or continuous variable) in mRCC patients treated with TKI agents.

RESULTS

Within 5,638 primitive records, 16 were eligible and 14 had adequate data for quantitative analysis (N = 2,863 participants). Random-effects meta-analysis showed that lower albumin was related to poorer OS (dichotomous: HR = 2.01, 95% CI: 1.64-2.46, P < 0.001, I² = 28.8%; continuous: HR =0.93, 95% CI: 0.86-1.00, P = 0.040, I² = 67.5%) and PFS (dichotomous: HR = 1.45, 95% CI: 1.04-2.01, P = 0.029, I² = 57.4%; continuous: HR = 0.89, 95% CI: 0.80-0.98, P = 0.023, I² = 93.3%).

CONCLUSION

Lower pre-treatment serum albumin level is an independent adverse predictor of prognosis of mRCC patients receiving TKI therapy.

REGISTRATION

PROSPERO ID: CRD42020196802 Sep. 2^nd, 2020.

Predictive value of De Ritis ratio in metastatic renal cell carcinoma treated with tyrosine-kinase inhibitors

BACKGROUND

Predictive markers can help tailor treatment to the individual in metastatic renal cell carcinoma (mRCC). De Ritis ratio (DRR) is associated with oncologic outcomes in various solid tumors.

OBJECTIVE

To assess the value of DRR in prognosticating survival in mRCC patients treated with tyrosine-kinase inhibitors (TKI).

METHODS

Overall, 220 mRCC patients treated with TKI first-line therapy were analyzed. An optimal cut-off point for DRR was determined with Youden's J. We used multiple strata for DRR, performed descriptive, Kaplan-Meier and multivariable Cox-regression analyses to assess associations of DRR with progression-free (PFS) and overall survival (OS).

RESULTS

Patients above the optimal cut-off point for DRR of ≥ 1.58 had fewer liver metastases (p = 0.01). There was no difference in PFS (p > 0.05) between DRR groups. DRR above the median of 1.08 (HR 1.42; p = 0.03), DRR ≥ 1.1(HR 1.44; p = 0.02), ≥ 1.8 (HR 1.56; p = 0.03), ≥ 1.9 (HR 1.59; p = 0.02) and ≥ 2.0 (HR 1.63; p = 0.047) were associated with worse OS. These associations did not remain after multivariable adjustment. In the intermediate MSKCC group, DRR was associated with inferior OS at cut-offs ≥ 1.0 (HR 1.78; p = 0.02), ≥ 1.1 (HR 1.81; p = 0.01) and above median (HR 1.88; p = 0.007) in multivariable analyses. In patients with clear-cell histology, DRR above median (HR 1.54; p = 0.029) and DRR ≥ 1.1 (HR 1.53; p = 0.029) were associated with OS in multivariable analyses.

CONCLUSION

There was no independent association between DRR and survival of mRCC patients treated with TKI in the entire cohort. However, OS of patients with intermediate risk and clear-cell histology were affected by DRR. DRR could be used for tailored decision-making in these subgroups.

Predictive role of γ-glutamyltransferase in patients receiving nivolumab therapy for metastatic renal cell carcinoma

INTRODUCTION

γ-Glutamyltransferase is reportedly associated with survival in local and metastatic renal cell carcinoma patients; however, its predictive role among patients treated with immune-checkpoint inhibitors are unknown. This study aimed to investigate the role of γ-glutamyltransferase as a predictive marker among metastatic renal cell carcinoma patients undergoing nivolumab therapy.

METHODS

We retrospectively evaluated 69 nivolumab-treated metastatic renal cell carcinoma patients upon failure of one or more systematic therapies. Serum γ-glutamyltransferase levels were determined at baseline and 2 months after nivolumab treatment initiation. Patients were classified as high (≥ 49 U/L) and low (< 49 mg/dL) from baseline GGT levels and the outcomes were compared between the two groups. Furthermore, increased (after/baseline ≥ 2) and non-increased (after/baseline < 2) groups were compared. Progression-free survival and overall survival were evaluated after nivolumab initiation.

RESULTS

Overall survival was significantly shorter in the high baseline γ-glutamyltransferase group (20.3%) than in the low group (79.7%) (median 2.33 vs not reached [months], p = 0.0051). Progression-free survival and the overall survival were significantly shorter in the increased than in the non-increased group (24.6% and 75.4%, respectively) (median PFS: 4.43 vs 7.23 [months], p = 0.0373/OS: 24.00 vs not reached, p = 0.0467). On multivariate analyses, high baseline γ-glutamyltransferase was an independent factor for overall survival (p = 0.0345) and increased γ-glutamyltransferase was an independent factor for progression-free survival (p = 0.0276) and overall survival (p = 0.0160).

CONCLUSIONS

High baseline γ-glutamyltransferase and its early increase are associated with a poor prognosis in metastatic renal cell carcinoma patients receiving nivolumab. Serum γ-glutamyltransferase levels may help predict treatment outcomes.

Association of preoperative serum De Ritis ratio with oncological outcomes in patients treated with cytoreductive nephrectomy for metastatic renal cell carcinoma

PURPOSE

Identifying which patients are likely to benefit from cytoreductive nephrectomy (CN) for metastatic renal cell carcinoma (mRCC) is important. We tested the association between preoperative serum De Ritis ratio (DRR, Aspartate Aminotransferase/Alanine Aminotransferase) and overall survival (OS) as well as cancer-specific survival (CSS) in mRCC patients treated with CN.

MATERIAL AND METHODS

mRCC patients treated with CN at different institutions were included. After assessing for the optimal pretreatment DRR cut-off value, we found 1.2 to have the maximum Youden index value. The overall population was therefore divided into 2 DRR groups using this cut-off (low, <1.2 vs. high, ≥1.2). Univariable and multivariable Cox regression analyses tested the association between DRR and OS as well as CSS. The discrimination of the model was evaluated with the Harrel's concordance index (C-index). The clinical value of the DRR was evaluated with decision curve analysis.

RESULTS

Among 613 mRCC patients, 239 (39%) patients had a DRR ≥1.2. Median follow-up was 31 (IQR 16-58) months. On univariable analysis, high DRR was significantly associated with OS (hazard ratios [HR]: 1.22, 95% confidence interval [CI]: 1.01-1.46, P = 0.04) and CSS (HR: 1.23, 95% CI: 1.02-1.47, P = 0.03). On multivariable analysis, which adjusted for the effect of established clinicopathologic features, high DRR remained significantly associated with both OS (HR: 1.26, 95% CI: 1.04-1.52, P = 0.02) and CSS (HR: 1.26, 95% CI: 1.05-1.53, P = 0.01). The addition of DRR only minimally improved the discrimination of a base model that included established clinicopathologic features (C-index = 0.633 vs. C-index = 0.629). On decision curve analysis, the inclusion of DRR did not improve the net-benefit beyond that obtained by established subgroup analyses stratified by IMDC risk groups, type of systemic therapy, body mass index and sarcomatoid features, did not reveal any prognostic value to DRR.

CONCLUSION

Despite the statistically significant association between DRR and OS as well as CSS in mRCC patients treated with CN, DRR does not seem to add any further prognostic value beyond that obtained by currently available features.

Distinction of Students and Expert Therapists Based on Therapeutic Motions on a Robotic Device Using Support Vector Machine

Purpose

To clarify the feature values of exercise therapy that can differentiate students and expert therapists and use this information as a reference for exercise therapy education.

Methods

The participants were therapists with 5 or more years of clinical experience and 4th year students at occupational therapist training schools who had completed their clinical practices. The exercise therapy task included Samothrace (code name, SAMO) exercises implemented on the elbow joint based on the elbow flexion angle, angular velocity, and exercise interval recordings. For analyses and student/therapist comparisons, the peak flexion angle, peak velocity, and movement time were calculated using data on elbow angle changes acquired via SAMO. Subsequently, bootstrap data were generated to differentiate between the exercise therapy techniques adopted by therapists and students, and a support vector machine was used to generate four types of data combinations with the peak flexion angle, peak velocity, and movement time values. These data were used to estimate and compare the respective accuracies with the Friedman test.

Results

The peak flexion angles were significantly smaller in the case of students. Furthermore, the peak velocities were larger, the peak flexion angles were smaller, and the movement times were shorter compared with those of therapists. The combination of peak velocity and peak flexion angle yielded the highest diagnostic accuracies.

Conclusion

When students and therapists performed upper limb exercise therapy techniques based on the kinematics movement of a robot arm, the movement speeds and joint angles differed. The combination of peak velocity and peak flexion angle was the most effective classifier used for the differentiation of the abilities of students and therapists. The peak velocity and peak flexion angle of the therapist group can be used as a reference for students when they learn upper limb therapeutic exercise techniques.

Machine-learning prediction of self-care activity by grip strengths of both hands in poststroke hemiplegia

To investigate the relationships between grip strengths and self-care activities in stroke patients using a non-linear support vector machine (SVM).Overall, 177 inpatients with poststroke hemiparesis were enrolled. Their grip strengths were measured using the Jamar dynamometer on the first day of rehabilitation training. Self-care activities were assessed by therapists using Functional Independence Measure (FIM), including items for eating, grooming, dressing the upper body, dressing the lower body, and bathing at the time of discharge. When each FIM item score was ≥6 points, the subject was considered independent. One thousand bootstrap grip strength datasets for each independence and dependence in self-care activities were generated from the actual grip strength. Thereafter, we randomly assigned the total bootstrap datasets to 90% training and 10% testing datasets and inputted the bootstrap training data into a non-linear SVM. After training, we used the SVM algorithm to predict a testing dataset for cross-validation. This validation procedure was repeated 10 times.The SVM with grip strengths more accurately predicted independence or dependence in self-care activities than the chance level (mean ± standard deviation of accuracy rate: eating, 0.71 ± 0.04, P < .0001; grooming, 0.77 ± 0.03, P < .0001; upper-body dressing, 0.75 ± 0.03, P < .0001; lower-body dressing, 0.72 ± 0.05, P < .0001; bathing, 0.68 ± 0.03, P < .0001).Non-linear SVM based on grip strengths can prospectively predict self-care activities.

Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

Purpose

Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists.

Methods

A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements.

Results

High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006).

Conclusion

This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.