scholarly journals IMG-20. RADIOMIC FEATURES IMPROVE PROGNOSTICATION OVER CONVENTIONAL MR DERIVED QUALITATIVE DESCRIPTORS IN PEDIATRIC SUPRATENTORIAL HIGH GRADE GLIOMA: COMPARISON OF MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii359-iii359
Author(s):  
John Lucas ◽  
Chih-Yang Hsu ◽  
Jared Becksfort ◽  
Scott Hwang ◽  
Zhaohua Lu ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Proportional Hazards ◽  
High Grade Glioma ◽  
Cox Proportional Hazards ◽  
Machine Learning Techniques ◽  
Imaging Features ◽  
High Grade ◽  
Fit Statistics ◽  
Prognostic Utility ◽  
Pre Treatment

Abstract PURPOSE/OBJECTIVES Pediatric supratentorial high-grade glioma (stHGG) is a biologically heterogeneous disease defined by unique mutations, natural history and prognosis. Prior work by our group outlined a role for qualitative imaging features in aiding prognostication. We build on that work by evaluating the prognostic utility of radiomic features (RM) when paired with clinical factors. MATERIALS/ METHODS Ninety-one patients age < 21 years with stHGG treated between 1980–2007 were retrospectively reviewed. Prognostic clinical, qualitative imaging (Visually AcceSAble Rembrandt Images, VASARI), and treatment characteristics were evaluated in concert with manual and automatically segmented (DeepMedic), tumor-derived semi-quantitative radiomic features (Pyradiomics) extracted from MR images. Prognostic RM were limited to stable imaging features which were subsequently selected using bootstrapped least absolute shrinkage and selection operator (LASSO). Nonparametric descriptive statistics and prognostication model evaluation, incorporating RM and clinical variables, were developed using random forest (RF), Cox proportional hazards (CPH), and deep learning (deepsurv) algorithms and assessed for goodness of fit using (c-index). RESULTS A subset (N=80) of 386 intensity, shape, and texture derived RM were stable between pre-treatment MR. 28 RM features were independently predictive of survival when compared to models utilizing combinations of clinical, VASARI and had comparable model fit statistics. CPH, RF and deepsurv showed comparable utility in modelling RM features. Combined modelling of clinical, VASARI and RM features using CPH, RF, and deepsurv resulted in c-indices of 0.68, 0.67, 0.68, respectively. CONCLUSION RM features are stable and independently prognostic. Combined modelling of clinical, VASARI, and RM features improves prognostication in stHGG.

scholarly journals NIMG-51. CONVENTIONAL MRI RADIOMIC FEATURES IMPROVE PROGNOSTICATION AND ARE PREDICTIVE OF H3 K27M STATUS IN DIPG

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii159-ii159
Author(s):  
Christopher Tinkle ◽  
Chih-Yang Hsu ◽  
Edward Simpson ◽  
Jason Chiang ◽  
Xiaoyu Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Automatic Segmentation ◽  
Cox Proportional Hazards ◽  
Survival Prediction ◽  
Imaging Features ◽  
K27m Mutation ◽  
The Impact

Abstract BACKGROUND Genomic profiling of DIPG suggests distinct and clinically relevant molecular subgroups based on the presence and isoform of histone H3 K27M mutation. We evaluated the impact of radiomic features on the classification and prognostication of 81 histologically confirmed and centrally reviewed DIPG. METHODS We utilized a combination of manual and automatic segmentation (DeepMedic) to define tumor volume and Pyradiomics for computation of radiomic features. Imaging feature stability was assessed by calculating concordance correlation coefficient (CCC) for each radiomic parameter from two separate pretreatment MRIs. Bootstrapped least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classification and prognostication models, incorporating H3 status and clinical variables, were developed using random forest, Cox proportional hazards, and deep learning algorithms and assessed for goodness of fit using the c-index. RESULTS Eighty of 386 imaging features demonstrated stability (CCC, p< 0.001) between pretreatment scans. LASSO identified 26 prognostic imaging features and 38 and 57 imaging features predictive of the presence of H3 K27M mutation and H3 K27M isoforms, respectively. Using five-fold cross validation, the accuracy of distinguishing H3 K27M mutant and WT tumors was 85% and 77% for H3.3 K27M, H3.1 K27M, and WT tumors. C-index for prognostication was 0.77 for Cox, 0.55 for random forest, and 0.72 for deep learning. All models were more predictive than the Jansen survival prediction model. CONCLUSIONS Stable, predictive radiomic features may be a surrogate for H3 status and enhance current prognostication of DIPG. Model validation in cohorts of prospectively treated patients with DIPG is ongoing.

scholarly journals RARE-53. THE ROLE OF RADIATION AND CHEMOTHERAPY IN ADULT PATIENTS WITH HIGH-GRADE BRAINSTEM GLIOMAS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi233-vi233
Author(s):  
Panagiotis Kerezoudis ◽  
Victor Lu ◽  
Mohammed Alvi ◽  
Anshit Goyal ◽  
Mohamad Bydon ◽  
...  
Keyword(s):  
Adjuvant Treatment ◽  
Proportional Hazards ◽  
Multivariable Analysis ◽  
High Grade Glioma ◽  
Tumor Grade ◽  
Cox Proportional Hazards ◽  
Adult Patients ◽  
High Grade ◽  
Specific Patient ◽  
Charlson Comorbidity Score

Abstract BACKGROUND High-grade gliomas (HGGs) of the brainstem represent a rarer subtype of central nervous system tumors compared to their supratentorial counterpart. Predictors of survival and patterns of care have not yet been established on a national, multi-institutional scale. METHODS The National Cancer Database was queried for adult cases surgically diagnosed with brainstem high-grade glioma. An array of patient demographics, comorbidities, tumor characteristics and treatment parameters were captured. Predictors of survival were investigated using multivariable Cox proportional hazards regression analysis adjusting for age, insurance status, Charlson comorbidity score, tumor grade, tumor size and type of treatment. RESULTS A total of 422 patients (median 51 years, 60% males) were analyzed. Two hundred eighty one received postoperative radiation with chemotherapy (66.6%), thirty-nine had radiation alone (9.2%), while the remaining had no adjuvant treatment (24.2%). Median radiation dosage was 54Gy. Overall median survival was 9.8 months (95% CI 8.8–12). Survival was significantly longer (p< .001) in the chemotherapy+radiation group (median: 14.2 months, 95% CI 11.7–17.1) compared to radiation alone (median: 5.7 months, 95% CI 3.7–12) and no adjuvant treatment (median:1.8 months, 95% CI 1.4–4). In multivariable analysis, increasing age (HR 1.87, 95% CI 1.47–2.37, p< .001) was associated with worse survival, whereas radiation with chemotherapy (HR 0.67, 95% CI 0.46–0.98, p=0.038) were associated with lower hazards of death compared to radiation alone. In subgroup analysis, the effect of adjuvant chemotherapy with radiation remained significant for grade IV (HR 0.46, 95% CI 0.28–0.76, p=0.003), but not for grade III tumors (HR 0.87, 95% CI 0.48–1.58, p=0.65). CONCLUSION Findings of the present analysis demonstrate the effectiveness of radiation with chemotherapy for adult patients with high-grade brainstem gliomas, particularly grade IV. Further research should aim on identifying specific patient profiles and molecular subgroups that are more likely to benefit from multimodality therapy.

Proliferation-dominant high-grade astrocytoma: survival benefit associated with extensive resection of FLAIR abnormality region

2020 ◽  
Vol 132 (4) ◽  
pp. 998-1005 ◽  
Author(s):  
Haihui Jiang ◽  
Yong Cui ◽  
Xiang Liu ◽  
Xiaohui Ren ◽  
Mingxiao Li ◽  
...  
Keyword(s):  
Survival Benefit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Characteristic Curve ◽  
Extent Of Resection ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
High Grade ◽  
Extensive Resection ◽  
High Grade Astrocytoma

OBJECTIVEThe aim of this study was to investigate the relationship between extent of resection (EOR) and survival in terms of clinical, molecular, and radiological factors in high-grade astrocytoma (HGA).METHODSClinical and radiological data from 585 cases of molecularly defined HGA were reviewed. In each case, the EOR was evaluated twice: once according to contrast-enhanced T1-weighted images (CE-T1WI) and once according to fluid attenuated inversion recovery (FLAIR) images. The ratio of the volume of the region of abnormality in CE-T1WI to that in FLAIR images (VFLAIR/VCE-T1WI) was calculated and a receiver operating characteristic curve was used to determine the optimal cutoff value for that ratio. Univariate and multivariate analyses were performed to identify the prognostic value of each factor.RESULTSBoth the EOR evaluated from CE-T1WI and the EOR evaluated from FLAIR could divide the whole cohort into 4 subgroups with different survival outcomes (p < 0.001). Cases were stratified into 2 subtypes based on VFLAIR/VCE-T1WIwith a cutoff of 10: a proliferation-dominant subtype and a diffusion-dominant subtype. Kaplan-Meier analysis showed a significant survival advantage for the proliferation-dominant subtype (p < 0.0001). The prognostic implication has been further confirmed in the Cox proportional hazards model (HR 1.105, 95% CI 1.078–1.134, p < 0.0001). The survival of patients with proliferation-dominant HGA was significantly prolonged in association with extensive resection of the FLAIR abnormality region beyond contrast-enhancing tumor (p = 0.03), while no survival benefit was observed in association with the extensive resection in the diffusion-dominant subtype (p=0.86).CONCLUSIONSVFLAIR/VCE-T1WIis an important classifier that could divide the HGA into 2 subtypes with distinct invasive features. Patients with proliferation-dominant HGA can benefit from extensive resection of the FLAIR abnormality region, which provides the theoretical basis for a personalized resection strategy.

scholarly journals Impact of Pathological Stratification on the Clinical Outcomes of Advanced Well-Differentiated/Dedifferentiated Liposarcoma Treated with Trabectedin

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1453
Author(s):  
Chiara Fabbroni ◽  
Giovanni Fucà ◽  
Francesca Ligorio ◽  
Elena Fumagalli ◽  
Marta Barisella ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Proportional Hazards ◽  
Case Series ◽  
Progression Free Survival ◽  
Cox Proportional Hazards ◽  
Low Grade ◽  
High Grade ◽  
Retrospective Case ◽  
Well Differentiated ◽  
The Impact

Background. We previously showed that grading can prognosticate the outcome of retroperitoneal liposarcoma (LPS). In the present study, we aimed to explore the impact of pathological stratification using grading on the clinical outcomes of patients with advanced well-differentiated LPS (WDLPS) and dedifferentiated LPS (DDLPS) treated with trabectedin. Patients: We included patients with advanced WDLPS and DDLPS treated with trabectedin at the Fondazione IRCCS Istituto Nazionale dei Tumori between April 2003 and November 2019. Tumors were categorized in WDLPS, low-grade DDLPS, and high-grade DDLPS according to the 2020 WHO classification. Patients were divided in two cohorts: Low-grade (WDLPS/low-grade DDLPS) and high-grade (high-grade DDLPS). Results: A total of 49 patients were included: 17 (35%) in the low-grade cohort and 32 (65%) in the high-grade cohort. Response rate was 47% in the low-grade cohort versus 9.4% in the high-grade cohort (logistic regression p = 0.006). Median progression-free survival (PFS) was 13.7 months in the low-grade cohort and 3.2 months in the high-grade cohort. Grading was confirmed as an independent predictor of PFS in the Cox proportional-hazards regression multivariable model (adjusted hazard ratio low-grade vs. high-grade: 0.45, 95% confidence interval: 0.22–0.94; adjusted p = 0.035). Conclusions: In this retrospective case series, sensitivity to trabectedin was higher in WDLPS/low-grade DDLPS than in high-grade DDLPS. If confirmed in larger series, grading could represent an effective tool to personalize the treatment with trabectedin in patients with advanced LPS.

scholarly journals Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shulun Nie ◽  
Yufang Zhu ◽  
Jia Yang ◽  
Tao Xin ◽  
Song Xue ◽  
...  
Keyword(s):  
Dna Methyltransferase ◽  
Goodness Of Fit ◽  
Clinical Target Volume ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
High Grade Glioma ◽  
Target Volume ◽  
Tumor Location ◽  
High Grade ◽  
Clinical Trial Registry

Abstract Introduction In this study, we performed a consecutive macropathologic analysis to assess microscopic extension (ME) in high-grade glioma (HGG) to determine appropriate clinical target volume (CTV) margins for radiotherapy. Materials and methods The study included HGG patients with tumors located in non-functional areas, and supratotal resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model. Results Between June 2017 and July 2019, 652 pathologic slides obtained from 30 HGG patients were analyzed. The mean ME distance was 1.70 cm (range, 0.63 to 2.87 cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O6-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: YME = 0.672 + 0.513XGrade + 0.380XSVZ + 0.439XMGMT + 0.320XIDH + 0.333X1p/19q. The R-square value of goodness of fit was 0.780. The receiver operating characteristic curve proved that the area under the curve was 0.964 (P < 0.001). Conclusion ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients. Trial registration The trial was registered with Chinese Clinical Trial Registry (ChiCTR2100046106). Registered 4 May 2021-Retrospectively registered.

scholarly journals Radiation Therapy for Retroperitoneal Sarcomas: Influences of Histology, Grade, and Size

Sarcoma ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jennifer L. Leiting ◽  
John R. Bergquist ◽  
Matthew C. Hernandez ◽  
Kenneth W. Merrell ◽  
Andrew L. Folpe ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Proportional Hazards ◽  
Multivariable Analysis ◽  
Cox Proportional Hazards ◽  
Preoperative Radiation ◽  
High Grade ◽  
Curative Intent ◽  
Kaplan Meier ◽  
Retroperitoneal Sarcomas ◽  
Ajcc Staging

Perioperative radiation therapy (RT) has been associated with reduced local recurrence in patients with retroperitoneal sarcomas (RPS); however, selection criteria remain unclear. We hypothesized that perioperative RT would improve survival in patients with RPS and would be associated with pathological factors. The National Cancer Database (NCDB) from 2004 to 2012 was reviewed for patients with nonmetastatic RPS undergoing curative intent resection. Tumor size was dichotomized at 15 cm based on 8th edition American Joint Committee on Cancer (AJCC) staging. Patients with the highest comorbidity score were excluded. Unadjusted Kaplan–Meier and adjusted Cox proportional hazards modeling analyzed overall survival (OS). Multivariable logistic regression modeled margin positivity. A total of 2,264 patients were included; 727 patients (32.1%) had perioperative radiation in whom 203 (9.0%) had radiation preoperatively. Median (IQR) RPS size was 17.5 [11.0–27.0] cm. Histopathology was high grade in 1048 patients (43.7%). Multivariable analysis revealed that perioperative radiation was independently associated with decreased mortality (HR 0.72, 95% confidence intervals (CIs) 0.62–0.84,p<0.001), and preoperative RT was associated with reduced margin positivity (HR 0.72, 95% CI 0.53–0.97,p=0.032). Stratified survival analysis showed that radiation was associated with prolonged median OS for RPS that were high-grade (64.3 vs. 43.6 months,p<0.001), less than 15 cm (104.1 vs. 84.2 months,p=0.007), and leiomyosarcomatous (104.8 vs. 61.8 months,p<0.001). Perioperative radiation is independently associated with decreased mortality in patients with high-grade, less than 15 cm, and leiomyosarcomatous tumors. Preoperative radiation is independently associated with margin-negative resection. These data support the selective use of perioperative radiation in the multidisciplinary management of RPS.

scholarly journals Prognostic Factors for Event-Free Survival in Pediatric Patients with Hepatoblastoma Based on the 2017 PRETEXT and CHIC-HS Systems

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1387 ◽  
Author(s):  
Hee Mang Yoon ◽  
Jisun Hwang ◽  
Kyung Won Kim ◽  
Jung-Man Namgoong ◽  
Dae Yeon Kim ◽  
...  
Keyword(s):  
Pediatric Patients ◽  
Proportional Hazards ◽  
Hazard Analysis ◽  
Staging System ◽  
Cox Proportional Hazards ◽  
Event Free Survival ◽  
Free Survival ◽  
Tertiary Referral Center ◽  
C Statistic ◽  
Pre Treatment

This study aimed to evaluate the prognostic value of variables used in the 2017 PRE-Treatment EXTent of tumor (PRETEXT) system and the Children’s Hepatic tumors International Collaboration-Hepatoblastoma Stratification (CHIC-HS) system in pediatric patients with hepatoblastoma. A retrospective analysis of data from the pediatric hepatoblastoma registry of a tertiary referral center was conducted to evaluate the clinical and imaging variables (annotation factors) of the PRETEXT staging system. The primary outcome was event-free survival (EFS). Data from 84 patients (mean age: 2.9 ± 3.5 years) identified between 1998 and 2017 were included. Univariable Cox proportional hazards analysis revealed that PRETEXT annotation factors P (portal vein involvement), F (multifocality of tumor), and M (distant metastasis) showed a significant negative association with EFS. Multivariable Cox proportional hazard analysis showed that factor F was the strongest predictor (HR (hazard ratio), 2.908; 95% CI (confidence interval), 1.061–7.972; p = 0.038), whereas factor M showed borderline significance (HR, 2.416; 95% CI, 0.918–6.354; p = 0.074). The prediction model based on F and M (F + M) showed good performance to predict EFS (C-statistic, 0.734; 95% CI, 0.612–0.854). In conclusion, the PRETEXT annotation factor F was the strongest predictor of EFS, and the F + M model showed good performance to predict EFS in pediatric patients with hepatoblastoma.

Application of a time-varying covariate model to the analysis of CA 19–9 as a biomarker for time-to-progression (TTP) and overall survival (OS) in patients with advanced pancreatic cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e15545-e15545
Author(s):  
S. Boeck ◽  
R. P. Laubender ◽  
M. Haas ◽  
C. Klose ◽  
F. Kullmann ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Proportional Hazards ◽  
Univariate Analysis ◽  
Locally Advanced ◽  
Predictive Biomarker ◽  
Cox Proportional Hazards ◽  
Time Varying ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression ◽  
Pre Treatment

e15545 Background: It remains unclear whether baseline CA 19–9 or CA 19–9 kinetics during chemotherapy may serve as predictive biomarker in patients (pts) with pancreatic cancer (PC). Methods: Main inclusion criteria for this retrospective multicenter analysis: histologically confirmed diagnosis of PC, treatment with first-line therapy, pre-treatment CA 19–9 level of > 5.2 U/ml. Analysis of CA 19–9 was exclusively performed using the Elecsys® assay (Roche Diagnostics). The effect of the pre- treatment CA 19–9 level on TTP and OS was modelled by Cox proportional hazards regression. The effect of CA 19–9 kinetics was also modelled by Cox proportional hazards regression where CA 19–9 was treated as time-varying covariate. When modelling CA 19–9 we developed univariate and multivariate Cox models where we selected additional predictors (e.g. performance status) using backward elimination performing likelihood ratio tests on a significance level of 0.05. Results: One-hundred and fifteen pts from 5 German centers were included. Median age was 63 years, 12% had locally advanced and 88% metastatic disease; 73 % of the pts were treated within prospective clinical trials. Median baseline CA 19–9 was 1059 U/ml (range 9.5–100000), median pre- treatment bilirubin 0.6 mg/dl. The median TTP in the study population was 4.4 months, median OS 9.4 months. Univariate analysis showed that the pre-treatment CA 19–9 level (as continuous variable, log [CA 19–9]) was significantly associated with TTP (HR 1.24, 95% CI 1.12–1.37, p<0.001) and OS (HR 1.16, 95% CI 1.06–1.28, p=0.002). These associations remained significant also within a multivariate analysis. For CA 19–9 kinetics during chemotherapy, data from 69 pts (TTP) and 84 pts (OS) were available, respectively; log [CA 19–9] kinetics were found to be a significant predictor for TTP in univariate (HR 1.44, 95% CI 1.25–1.67, p<0.001) and multivariate (HR 1.39, 95% CI 1.19–1.62, p<0.001) analyses, and also for OS (univariate: HR 1.34, 95% CI 1.20–1.49, p<0.001; multivariate: HR 1.39, 95% CI 1.23–1.57, p<0.001). Conclusions: According to this new statistical model, CA 19–9 may serve as a useful predictive biomarker in advanced PC. [Table: see text]

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