scholarly journals AGLR is a novel index for the prognosis of hepatocellular carcinoma patients: a retrospective study

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yan Liao ◽  
Rongyu Wei ◽  
Renzhi Yao ◽  
Liling Qin ◽  
Jun Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Validation Cohort ◽  
Multivariate Analyses ◽  
The Novel ◽  
Gamma Glutamyl Transpeptidase ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Scoring Model ◽  
Kaplan Meier

Abstract Background Most hepatocellular carcinoma (HCC) patients’ liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase)/lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. Methods A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan–Meier method. Results According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with alpha-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI 1.21–2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI 1.35–2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis. Conclusions The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

scholarly journals AGLR is a Novel Index for the Prognosis of Hepatocellular Carcinoma Patients: A Retrospective Study

2020 ◽  
Author(s):  
Yan Liao ◽  
Rongyu Wei ◽  
Renzhi Yao ◽  
Liling Qin ◽  
Jun Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Validation Cohort ◽  
Multivariate Analyses ◽  
The Novel ◽  
Gamma Glutamyl Transpeptidase ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Scoring Model ◽  
Kaplan Meier

Abstract Background: Most hepatocellular carcinoma (HCC) patients’ liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase) / lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. Methods: A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan-Meier method.Results: According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with α-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI, 1.21-2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI, 1.35-2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis.Conclusions: The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

scholarly journals Prognostic value of preoperative inflammatory markers in patients with hepatocellular carcinoma underwent curative resection

2021 ◽  
Author(s):  
Wenlong Wu ◽  
Quancheng Wang ◽  
Dandan Han ◽  
Jianhui Li ◽  
Ye Nie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Inflammatory Markers ◽  
Curative Resection ◽  
Information Criterion ◽  
Gamma Glutamyl Transpeptidase ◽  
Roc Curve Analysis ◽  
Lymphocyte Ratio ◽  
Kaplan Meier ◽  
Glutamyl Transpeptidase

Abstract Background: The prognosis of hepatocellular carcinoma (HCC) is not optimistic. Our study focused on present inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), gamma-glutamyl transpeptidase-to-platelet ratio (GPR), aspartate aminotransferase-to-lymphocyte ratio (ALR) and fibrinogen-to-albumin ratio (FAR), and aimed to explore their optimal combination for the prognosis of HCC after resection.Methods: 347 HCC patients with curative resection were enrolled. The optimal cutoff values of the inflammatory markers were calculated using receiver operating characteristic (ROC) curve analysis, and used to divide patients into two groups whose differences were compared by Kaplan-Meier analysis. Cox univariate and multivariate analysis were used to analyze the independent prognostic inflammatory markers. c2 test was chosen to determine the relationship between independent prognostic inflammatory markers and clinicopathological features. We created the combined scoring models and evaluated them by Cox univariate and multivariate methods. The concordance index (C-index), Akaike information criterion (AIC) and likelihood ratio were calculated to compare the models. The selected optimal inflammatory markers and their combinations were tested in different stages of HCC by Kaplan-Meier analysis.Results: ALR and GPR were independent prognostic factors for DFS; ALR, PLR, and GPR were independent prognostic factors for OS. The proposed GPR and ALR-GPR-PLR score models were independent predictors for DFS and OS, respectively.Conclusion: The preoperative GPR and ALR-GPR-PLR score models were independent predictors for DFS and OS, respectively, and performed well in stratifying patients with HCC. The higher score in the model, the worse the prognosis was.

Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

scholarly journals Prognostic value of preoperative inflammatory markers in patients with hepatocellular carcinoma who underwent curative resection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenlong Wu ◽  
Quancheng Wang ◽  
Dandan Han ◽  
Jianhui Li ◽  
Ye Nie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Inflammatory Markers ◽  
Curative Resection ◽  
Disease Free Survival ◽  
Information Criterion ◽  
Gamma Glutamyl Transpeptidase ◽  
Roc Curve Analysis ◽  
Lymphocyte Ratio ◽  
Kaplan Meier

Abstract Background The prognosis of hepatocellular carcinoma (HCC) is not optimistic. Our study focused on present inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), gamma-glutamyl transpeptidase-to-platelet ratio (GPR), aspartate aminotransferase-to-lymphocyte ratio (ALR) and fibrinogen-to-albumin ratio (FAR), and explored their optimal combination for the prognosis of HCC after resection. Methods A total of 347 HCC patients who underwent curative resection were enrolled. The optimal cutoff values of the inflammatory markers were calculated using receiver operating characteristic (ROC) curve analysis, and used to divide patients into two groups whose differences were compared by Kaplan–Meier analysis. Cox univariate and multivariate analyses were used to analyze the independent prognostic inflammatory markers. The χ2 test was chosen to determine the relationship between independent prognostic inflammatory markers and clinicopathological features. We created combined scoring models and evaluated them by Cox univariate and multivariate methods. The concordance index (C-index), Akaike information criterion (AIC) and likelihood ratio were calculated to compare the models. The selected optimal inflammatory markers and their combinations were tested in different stages of HCC by Kaplan–Meier analysis. Results The ALR and GPR were independent prognostic factors for disease-free survival (DFS); the ALR, PLR, and GPR were independent prognostic factors for overall survival (OS). The proposed GPR and ALR-GPR-PLR score models were independent predictors for DFS and OS, respectively. Conclusion The preoperative GPR and ALR-GPR-PLR score models were independent predictors for DFS and OS, respectively, and performed well in stratifying patients with HCC. The higher the score in the model was, the worse the prognosis.

Baseline apparent diffusion coefficients: Validation study of new predictor of survival in patients with unresectable hepatocellular carcinoma following chemoembolization

2021 ◽  
pp. 1-10
Author(s):  
Lichao Xu ◽  
Shiqin Wang ◽  
Shengping Wang ◽  
Ying Wang ◽  
Wentao Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Response To Treatment ◽  
Roc Curve Analysis ◽  
Adc Value ◽  
Apparent Diffusion Coefficients ◽  
Apparent Diffusion ◽  
Response Biomarker ◽  
The Difference

OBJECTIVES: To investigate whether the baseline apparent diffusion coefficient (ADC) can predict survival in the hepatocellular carcinoma (HCC) patients receiving chemoembolization. MATERIALS AND METHODS: Diffusion-weighted MR imaging of HCC patients is performed within 2 weeks before chemoembolization. The ADC of the largest index lesion is recorded. Responses are assessed by mRECIST after the start of the second course of chemoembolization. Receiver operating characteristic (ROC) curve analysis is performed to evaluate the diagnostic performance and determine optimal cut-off values. Cox regression and Kaplan–Meier survival analyses are used to explore the differences in overall survival (OS) between the responders and non-responders. RESULTS: The difference is statistically significant in the baseline ADC between the responders and non-responders (P <  0.001). ROC analyses indicate that the baseline ADC value is a good predictor of response to treatment with an area under the ROC curve (AUC) of 0.744 and the optimal cut-off value of 1.22×10–3 mm2/s. The Cox regression model shows that the baseline ADC is an independent predictor of OS, with a 57.2% reduction in risk. CONCLUSION: An optimal baseline ADC value is a functional imaging response biomarker that has higher discriminatory power to predict tumor response and prolonged survival following chemoembolization in HCC patients.

scholarly journals A preoperative nomogram predicts prognosis of patients with hepatocellular carcinoma after liver transplantation: a multicenter retrospective study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dabing Huang ◽  
Yinan Shen ◽  
Wei Zhang ◽  
Chengxiang Guo ◽  
Tingbo Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Adjuvant Therapy ◽  
Validation Cohort ◽  
Multivariate Analyses ◽  
Meld Score ◽  
Blood Type ◽  
Intermediate Risk ◽  
Final Model ◽  
Using Data

Abstract Background Although criteria for liver transplantation, such as the Milan criteria and Hangzhou experiences, have become popular, criteria to guide adjuvant therapy for patients with hepatocellular carcinoma after liver transplantation are lacking. Methods We collected data from all consecutive patients from 2012 to 2019 at three liver transplantation centers in China retrospectively. Univariate and multivariate analyses were used to analyze preoperative parameters, such as demographic and clinical data. Using data obtained in our center, calibration curves and the concordance Harrell’s C-indices were used to establish the final model. The validation cohort comprised the patients from the other centers. Results Data from 233 patients were used to construct the nomogram. The validation cohort comprised 36 patients. Independent predictors of overall survival (OS) were identified as HbeAg positive (P = 0.044), blood-type compatibility unmatched (P = 0.034), liver transplantation criteria (P = 0.003), and high MELD score (P = 0.037). For the validation cohort, to predict OS, the C-index of the nomogram was 0.874. Based on the model, patients could be assigned into low-risk (≥ 50%), intermediate-risk (30–50%), and high-risk (≤ 30%) groups to guide adjuvant therapy after surgery and to facilitate personalized management. Conclusions The OS in patients with hepatocellular carcinoma after liver transplantation could be accurately predicted using the developed nomogram.

scholarly journals Albumin platelet product as a novel score for liver fibrosis stage and prognosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Koji Fujita ◽  
Kazumi Yamasaki ◽  
Asahiro Morishita ◽  
Tingting Shi ◽  
Joji Tani ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Validation Cohort ◽  
Activity Grade ◽  
Fibrosis Stage ◽  
Strongly Correlated ◽  
The Novel ◽  
Training Cohort ◽  
Blood Examination ◽  
Retrospective Multicenter Study ◽  
Overall Mortality

AbstractFibrosis-4 index, a conventional biomarker for liver fibrosis stage, is confounded by age and hepatitis activity grade. The current retrospective multicenter study aimed to formulate the novel indices of liver fibrosis by mathematically combining items of peripheral blood examination and to evaluate ability of prognosis prediction. After a novel index was established in a training cohort, the index was tested in a validation cohort. Briefly, a total of 426 patients were enrolled in a training cohort. Albumin and platelet most strongly correlated to fibrosis stage among blood examination. Albumin platelet product (APP) = Albumin × platelet/1000 could differentiate the four stages of liver fibrosis (p < 0.05). APP indicated fibrosis stage independent from hepatitis activity grade. A cut-off value = 4.349 diagnosed cirrhosis with area under ROC more than 0.8. Multivariate analysis revealed that smaller APP independently contributed to HCC prevalence and overall mortality. The results were validated in another 707 patients with HCV infection. In conclusion, APP was not confounded by age or hepatitis activity grade contrary to Fibrosis-4 index. APP is as simple as physicians can calculate it by pen calculation. The product serves physicians in managing patients with chronic liver disease.

P2263Prognostic impact of perceived social isolation in patients with heart failure

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
T Ikeda ◽  
K Iwatsu ◽  
K Matsumura ◽  
H Ashikawa ◽  
K Takabayashi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Social Isolation ◽  
Roc Curve ◽  
Negative Impact ◽  
Roc Curve Analysis ◽  
Rehospitalization Rate ◽  
Kaplan Meier ◽  
Patients With Heart Failure ◽  
Perceived Social Isolation ◽  
The Relationship

Abstract Background Perceived social isolation (SI), the subjective sense of feelings of loneliness or isolation, has a negative impact on health outcomes, particularly in older adults. Although SI may also contribute to poor prognosis in patients with HF, evidence on the relationship between SI and outcomes in patients with HF is limited. Purpose The aim of this study was to investigate the relationship between SI and hospital readmission in patients with HF. Methods This study was a single center prospective cohort study. We consecutively enrolled 203 patients (mean age 72.9±11.7) who admitted for acute HF or exacerbation of chronic HF. At hospital discharge, we assessed perceived SI by using Lubben Social Network Scale - 6 (LSNS-6). Lower scores in LSNS-6 represents greater SI. Study outcome was rehospitalization for worsening HF within 180 days after discharge. We selected the optimal cutoff point of LSNS-6 that predict a worse outcome by the receiver operating characteristic (ROC) curve analysis. We investigate the association between SI and 180-days HF rehospitalization by using Cox proportional-hazard models, controlling for potential confounding factors. Results During follow up, A total of 40events (19.7%) were observed. The optimal cut-off point of LSNS-6 score was 17 points (the area under the ROC curve: 0.62, p<0.05, sensitivity: 82.5%, specificity 42.4%). Kaplan-Meier survival curves showed that those patients with greater SI (LSNS-6≤17) presented significantly higher HF rehospitalization rate (Figure). After adjusting for several pre-existing prognostic factors, LSNS-6≤17 was independently associated with HF rehospitalization (hazard ratio2.15,95% confidence interval 1.00–4.89). Conclusion The present study shows that SI is a independent predictor of HF rehospitalization in patients with HF. Assessing SI in the clinical practice with a brief screening tool may help identify patients with heart failure at greater risk of rehospitalization.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen &gt;2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p &lt; 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter &gt;4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p &lt; 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer patients with liver metastases or other metastatic patterns.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 435-435
Author(s):  
Junjie Hang ◽  
Lixia Wu
Keyword(s):  
Pancreatic Cancer ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Advanced Pancreatic Cancer ◽  
Region Of Interest ◽  
Calibration Plot ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Metastatic Patterns

435 Background: Pancreatic cancer patients with liver metastases had much poorer prognosis than those with other metastatic patterns. This study aimed to develop and validate a radiomics model to discriminate pancreatic cancer patients with liver metastases from patients with other metastatic patterns. Methods: We evaluated 77 patients advanced pancreatic cancer (APC) with different metastatic patterns and performed texture analysis on the region of interest (ROI). 58 patients and 19 patients were allocated randomly into the training cohort and the validation cohort with almost the same proportion of patients with liver metastases. An independent samples t-test was used for initial feature selection in the training cohort. Random Forest Classifier (RFC) was used to construct models based on these features in both cohorts and a radiomics signature (RS) was derived from the model. Then a nomogram was constructed based on RS and CA19-9, and validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods. Results: A nomogram based on the RS and CA19-9 was constructed and it demonstrated good discrimination in the training cohort (AUC = 0.93) and validation cohort (AUC = 0.81). Kaplan-meier methods showed that patients with RS>0.61 had much poorer OS than patients with RS < 0.61 in both cohorts. Conclusions:This study presents a radiomics nomogram incorporating both RS and CA19-9, which can be used to discriminate advanced pancreatic cancer patients with liver metastases from patients with other metastatic patterns.

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