Benjamin James Rich

Dr. Benjamin James Rich

orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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14 November 2021 in Cancers
Verified
Journal Article
Open access
PDF file
BRBenjamin James Rich
Benjamin James Rich
JHJianfeng Huang
Jianfeng Huang
YYYidong Yang
Yidong Yang
WJWilliam Jin
William Jin
PJPerry Johnson
Perry Johnson
FYFei Yang
Fei Yang
(1) Background and purpose: clinical trials have unsuccessfully tried to de-escalate treatment in locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) with the goal of reducing treatment toxicity. The aim of this study was to explore the role of radiomics for risk stratification in this patient population to guide treatment. (2) Methods: the study population consisted of 225 patients with locally advanced HPV+ OPSCC treated with curative-intent radiation or chemoradiation therapy. Appearance of distant metastasis was used as the endpoint event. Radiomics data were extracted from the gross tumor volumes (GTVs) identified on the planning CT, with gray level being discretized using three different bin widths (8, 16, and 32). The data extracted for the groups with and without distant metastasis were subsequently balanced using three different algorithms including synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and borderline SMOTE. From these different combinations, a total of nine radiomics datasets were derived. Top features that minimized redundancy while maximizing relevance to the endpoint were selected individually and collectively for the nine radiomics datasets to build support vector machine (SVM) based predictive classifiers. Performance of the developed classifiers was evaluated by receiver operating characteristic (ROC) curve analysis. (3) Results: of the 225 locally advanced HPV+ OPSCC patients being studied, 9.3% had developed distant metastases at last follow-up. SVM classifiers built for the nine radiomics dataset using either their own respective top features or the top consensus ones were all able to differentiate the two cohorts at a level of excellence or beyond, with ROC area under curve (AUC) ranging from 0.84 to 0.95 (median = 0.90). ROC comparisons further revealed that the majority of the built classifiers did not distinguish the two cohorts significantly better than each other. (4) Conclusions: radiomics demonstrated discriminative ability in distinguishing patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone and may serve to risk stratify this patient population with the purpose of guiding the appropriate therapy.

4 June 2022 in International Journal of Radiation Oncology, Biology, Physics
Verified
Journal Article
Closed access
BRBenjamin James Rich
Benjamin James Rich
CMChris Montoya
Chris Montoya
WJWilliam H. Jin
William H. Jin
BSBenjamin O. Spieler
Benjamin O. Spieler
BMBrandon A. Mahal
Brandon A. Mahal
APAlan Dal Pra
Alan Dal Pra

5 June 2024 in World Neurosurgery
Verified
Journal Article
Closed access
DJDanny John
Danny John
MAMohammed Alshalalfa
Mohammed Alshalalfa
TATimoteo Almeida
Timoteo Almeida
AMAnna Murray
Anna Murray
JMJoao Marques
Joao Marques
BRBenjamin James Rich
Benjamin James Rich

23 August 2022 in World Neurosurgery
Verified
Journal Article
Closed access
BRBenjamin James Rich
Benjamin James Rich
DKDeukwoo Kwon
Deukwoo Kwon
YSYash S. Soni
Yash S. Soni
JBJonathan B. Bell
Jonathan B. Bell
DJDanny John
Danny John
TDTejan Diwanji
Tejan Diwanji

5 September 2022 in Cureus
Verified
Journal Article
Open access
BCBrianna Conte
Brianna Conte
BRBenjamin James Rich
Benjamin James Rich
SGSakir H. Gultekin
Sakir H. Gultekin
GAGregory Azzam
Gregory Azzam
MEMaria Del Pilar Guillermo Prieto Eibl
Maria Del Pilar Guillermo Prieto Eibl
Glioblastoma, isocitrate dehydrogenase (IDH) wild type, is an aggressive primary brain malignancy with a poor prognosis, despite treatment including surgery, chemotherapy, and radiation therapy. Few patients with glioblastoma develop metastasis outside the neuroaxis, likely due to disease progression in the brain prior to extraneural dissemination. The driving mutations of tumors in patients with extraneural metastases are not well described. In this case, we present a severe case of extraneural metastatic glioblastoma, as well as the genetic mutations of the tumor.

21 February 2022 in Cureus
Verified
Journal Article
Open access
WJWilliam Jin
William Jin
BRBenjamin James Rich
Benjamin James Rich
RYRaphael Yechieli
Raphael Yechieli
LFLaura Freedman
Laura Freedman
MSMichael A Samuels
Michael A Samuels
SSStuart Samuels
Stuart Samuels
The objective of this study is to contrast the predictive ability of targeted muscle groups as radiographic proxies of sarcopenia on computerized tomography (CT) with body mass index (BMI) in head and neck cancer patients (H&NCP) undergoing radiation at a safety net hospital, and to evaluate sarcopenia with survival, local progression, toxicities and treatment delays. A retrospective review included 52 H&NCP treated between 2017-2019. The posterior neck muscles (PN), sternocleidomastoids (SCM), and their summed volume (AM) were contoured at C3 on patients’ pre-treatment CT scans, then normalized to obtain skeletal muscle index (MI) values. Pre-treatment BMI was also evaluated. Cutoffs for sarcopenia were determined by receiver operating characteristic curves. Overall survival and local recurrence-free survival were evaluated by Kaplan-Meier. Acute grade 3 or higher toxicities were evaluated by binomial logistic regression. Using all neck muscles (AM-MI) produced the best model for predicting outcomes, outperforming individual muscle groups and BMI. Local progression-free survival was worse in sarcopenic patients at 25.81 months versus 35.40 months (p=0.026). Acute grade 3 or higher toxicities were associated with sarcopenia (p=0.005). In this small, retrospective single-institution experience at a safety net hospital, a single axial slice of the combined sternocleidomastoids and paravertebral muscles at C3 performed better than either muscle group alone or pre-treatment BMI at predicting oncologic outcomes.