Extraneural Metastases From a High-Grade Glioma (HGG) With an H3F3A G34R Mutation

Distant metastatic disease from gliomas is extremely rare. We report the case of a 17-year-old female with an H3F3A G34R mutated infiltrative glioma who developed painful osseous metastases to her pelvis and spine within 3 months of clinical presentation. The presence of an H3F3A mutation in these patients may indicate further work-up to include intensive staging examination

Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review

BackgroundRadiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.MethodsLiterature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.ResultsSixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].ConclusionAlthough MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data

KRAS mutation status in the prediction of pancreatic tumor response after neoadjuvant systemic therapy and magnetic resonance-guided SBRT

709 Background: Stereotactic body radiotherapy (SBRT) has been incorporated into multi-modality treatment of locally advanced and borderline resectable pancreatic ductal adenocarcinoma (PDAC). For non-metastatic inoperable PDAC patients (pts) who receive magnetic resonance-guided SBRT (MRgSBRT), baseline features that predict for treatment response remain unsettled. In localized PDAC, multiple studies have investigated the KRAS oncogene as a prognostic factor with mixed findings. In this single institution retrospective study of PDAC pts treated with MRgSBRT, we hypothesized that KRAS mutation status would be predictive of meaningful clinical outcomes. Methods: From an IRB approved dataset of PDAC pts treated with MRgSBRT between 2016 and 2022, 39 pts with non-metastatic inoperable pancreatic cancer, known KRAS mutation status, ≥ 3 months of neoadjuvant systemic therapy (NST), and at least 3 months post-RT follow up were extracted for analysis. Baseline demographics, tumor and treatment characteristics, and clinical endpoints including conversion to resectability, best imaging response per RECIST v1.1, pathologic response (PR) per TRG-CAP, and overall survival (OS) after MRgSBRT were collected. Objective response (ORR) on both imaging and pathology was defined as complete response (CR) + partial response (PR); disease control (DC) was defined as CR + PR + stable disease (SD). Logistic regression was used to assess correlation between baseline variables and ORR. Cox proportional hazard models were utilized to determine association with OS. Results: Out of 39 pts, 21 (53%) were KRAS-mutated (KRAS-mt) and 18 (47%) were KRAS wild-type (KRAS-wt). Median age was 62, 54% were male, only 1 pt had ECOG > 1, and CA 19-9 at diagnosis was 197. Median duration of NST was 9 cycles, and common agents included Gemcitabine and Abraxane (21%); FOLFIRINOX (28%); a combination of the two regimens (41%); or other NSTs (10%). Median MRgSBRT dose fractionation was 50 Gy (range 35 – 50) in 5 fractions; all fractions underwent adaptive optimization. Thirty pts (77%) experienced DC on imaging, 16 (43%) had ORR, and 12 (31%) were converted to resectable following MRgSBRT. Cohort median OS was 12.3 months, and pts who had surgery had a non-significant median OS advantage (18 vs 12 months; p=0.19). KRAS-mt was associated with worse ORR (p=0.04; AUC=0.73) and decreased OS (HR: 2.15; p=0.03). In a clinical model incorporating baseline characteristics (age, ECOG, radiographic staging, CA 19-9 level, and KRAS), only KRAS predicted OS (HR: 3.02 for KRAS-mt; p=0.01). KRAS-mt status was also predictive of OS among pts who underwent surgery (HR: 5.24 for KRAS-mt; p=0.04). Conclusions: For localized PDAC pts inoperable at diagnosis who received NST followed by MRgSBRT, KRAS was the only significant predictor of ORR and OS, highlighting the need for further treatment intensification in KRAS-mt pts

data_sheet_3_Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review.xlsx

Background<p>Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.</p>Methods<p>Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.</p>Results<p>Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].</p>Conclusion<p>Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.</p

data_sheet_1_Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review.xlsx

Background<p>Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.</p>Methods<p>Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.</p>Results<p>Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].</p>Conclusion<p>Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.</p

data_sheet_4_Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review.xlsx

Background<p>Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.</p>Methods<p>Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.</p>Results<p>Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].</p>Conclusion<p>Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.</p

data_sheet_2_Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review.xlsx

Background<p>Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.</p>Methods<p>Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.</p>Results<p>Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].</p>Conclusion<p>Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.</p

Non-English language validation of patient-reported outcome measures in cancer clinical trials

Patient-reported outcome measures (PROMs) are increasingly incorporated as endpoints in oncology clinical trials but are often only validated in English. ClinicalTrials.gov was queried for cancer-specific randomized control trials (RCTs) addressing a therapeutic intervention and enrolling primarily in the USA. Peer-reviewed validation of Spanish and Chinese versions of each PROM was assessed. Of 103 eligible trials, a PROM was used as a primary endpoint in 25 RCTs (24.3%) and as a secondary endpoint in 78 RCTs (75.7%). A total of 61 of the 103 eligible trials (59.2%) and 17 of the 25 trials with a PROM primary endpoint (68.0%) used a PROM with either no Spanish or Chinese validation. The absence of validated PROM translations may diminish the voices of non-English language speaking trial participants. With an increasingly diverse US population, validation of non-English PROM translations may decrease disparities in trial participation and improve generalizability of study results