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Article ; Online: Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features

Harsh Patel / David M. Vock / G. Elisabeta Marai / Clifton D. Fuller / Abdallah S. R. Mohamed / Guadalupe Canahuate

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 11

Abstract: Abstract To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation ( ... ...

Abstract	Abstract To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan–Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation

Abdallah S.R. Mohamed / Geoffrey V. Martin / Sweet Ping Ng / Vinita Takiar / Beth M. Beadle / Mark Zafereo / Adam S. Garden / Steven J. Frank / C. David Fuller / G. Brandon Gunn / William H. Morrison / David I. Rosenthal / Jay Reddy / Amy Moreno / Anna Lee / Jack Phan

Clinical and Translational Radiation Oncology, Vol 44, Iss , Pp 100700- (2024)

1481

Abstract: Purpose/Objectives: The purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/Methods: Patterns of ... ...

Abstract	Purpose/Objectives: The purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/Methods: Patterns of LRR for 61 patients treated consecutively between 2003 and 2014 who received post-operative IMRT reirradiation to ≥ 60 Gy for recurrent HNSCC were determined by 2 methods: 1) physician classification via visual comparison of post-radiotherapy imaging to reirradiation plans; and 2) using deformable image registration (DIR). Those without evaluable CT planning image data were excluded. All recurrences were verified by biopsy or radiological progression. Failures were defined as in-field, marginal, or out-of-field. Logistic regression analyses were performed to identify predictors for LRR. Results: A total of 55 patients were eligible for analysis and 23 (42 %) had documented LRR after reirradiation. Location of recurrent disease prior to salvage surgery (lymphatic vs. mucosal) was the most significant predictor of LRR after post-operative reirradiation with salvage rate of 67 % for lymphatic vs. 33 % for mucosal sites (p = 0.037). Physician classification of LRR yielded 14 (61 %) in-field failures, 3 (13 %) marginal failures, and 6 (26 %) out-of-field failures, while DIR yielded 10 (44 %) in-field failures, 4 (17 %) marginal failures, and 9 (39 %) out-of-field failures. Most failures (57 %) occurred within the original site of recurrence or first echelon lymphatic drainage. Of patients who had a free flap placed during salvage surgery, 56 % of failures occurred within 1 cm of the surgical flap. Conclusion: Our study highlights the role of DIR in enhancing the accuracy and consistency of POF analysis. Compared to traditional visual inspection, DIR reduces interobserver variability and provides more nuanced insights into dose-specific and spatial parameters of locoregional recurrences. Additionally, the study identifies the location of the initial recurrence as a key ...
Keywords	Head and neck ; Squamous cell carcinoma ; Reirradiation ; Surgery ; Patterns of failure ; DIR ; Medical physics. Medical radiology. Nuclear medicine ; R895-920 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Subject code	616
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

Kareem A. Wahid / Diana Lin / Onur Sahin / Michael Cislo / Benjamin E. Nelms / Renjie He / Mohammed A. Naser / Simon Duke / Michael V. Sherer / John P. Christodouleas / Abdallah S. R. Mohamed / James D. Murphy / Clifton D. Fuller / Erin F. Gillespie

Scientific Data, Vol 10, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the ... ...

Abstract	Abstract Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.
Keywords	Science ; Q
Subject code	004
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An algorithm for thoracic re-irradiation using biologically effective dose

Eric D. Brooks / Xiaochun Wang / Brian De / Vivek Verma / Tyler D. Williamson / Rachel Hunter / Abdallah S. R. Mohamed / Matthew S. Ning / Xiaodong Zhang / Joe Y. Chang

Radiation Oncology, Vol 17, Iss 1, Pp 1-

a common language on how to treat in a “no-treat zone”

2022 Volume 11

Abstract: Abstract Background Re-irradiation (re-RT) is a technically challenging task for which few standardized approaches exist. This is in part due to the lack of a common platform to assess dose tolerance in relation to toxicity in the re-RT setting. To ... ...

Abstract	Abstract Background Re-irradiation (re-RT) is a technically challenging task for which few standardized approaches exist. This is in part due to the lack of a common platform to assess dose tolerance in relation to toxicity in the re-RT setting. To better address this knowledge gap and provide new tools for studying and developing thresholds for re-RT, we developed a novel algorithm that allows for anatomically accurate three-dimensional mapping of composite biological effective dose (BED) distributions from nominal doses (Gy). Methods The algorithm was designed to automatically convert nominal dose from prior treatment plans to corresponding BED value maps (voxel size 2.5 mm3 and α/β of 3 for normal tissue, BED3). Following the conversion of each plan to a BED3 dose distribution, deformable registration was used to create a summed composite re-irradiation BED3 plan for each patient who received two treatments. A proof-of-principle analysis was performed on 38 re-irradiation cases of initial stereotactic ablative radiotherapy (SABR) followed by either re-SABR or chemoradiation for isolated locoregional recurrence of early-stage non-small cell lung cancer. Results Evaluation of the algorithm-generated maps revealed appropriate conversion of physical dose to BED at each voxel. Of 14 patients receiving repeat SABR, there was one case each of grade 3 chest wall pain (7%), pneumonitis (7%), and dyspnea (7%). Of 24 patients undergoing repeat fractionated radiotherapy, grade 3 events were limited to two cases each of pneumonitis and dyspnea (8%). Composite BED3 dosimetry for each patient who experienced grade 2–3 events is provided and may help guide development of precise cumulative dose thresholds for organs at risk in the re-RT setting. Conclusions This novel algorithm successfully created a voxel-by-voxel composite treatment plan using BED values. This approach may be used to more precisely examine dosimetric predictors of toxicities and to establish more accurate normal tissue constraints for re-irradiation.
Keywords	Re-irradiation ; Biologically effective dose ; Equivalent dose ; Dosimetry ; Stereotactic ablative radiotherapy ; Stereotactic body radiation therapy ; Medical physics. Medical radiology. Nuclear medicine ; R895-920 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Subject code	616
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dynamics-Adapted Radiotherapy Dose (DARD) for Head and Neck Cancer Radiotherapy Dose Personalization

Mohammad U. Zahid / Abdallah S. R. Mohamed / Jimmy J. Caudell / Louis B. Harrison / Clifton D. Fuller / Eduardo G. Moros / Heiko Enderling

Journal of Personalized Medicine, Vol 11, Iss 1124, p

2021 Volume 1124

Abstract: Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66–70 Gy, this is done ... ...

Abstract	Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66–70 Gy, this is done without considering patient heterogeneity. We present a framework to estimate a personalized RT dose for individual patients, based on pre- and early on-treatment tumor volume dynamics—a dynamics-adapted radiotherapy dose ( D DARD ). We also present the results of an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66–70 Gy RT in 2–2.12 Gy weekday fractions. This trial was repeated constraining D DARD between (54, 82) Gy to test more moderate dose adjustment. D DARD was estimated to range from 8 to 186 Gy, and our in silico trial estimated that 77% of patients treated with standard of care were overdosed by an average dose of 39 Gy, and 23% underdosed by an average dose of 32 Gy. The in silico trial with constrained dose adjustment estimated that locoregional control could be improved by >10%. We demonstrated the feasibility of using early treatment tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. These results demonstrate the potential to both de-escalate most patients, while still improving population-level control rates.
Keywords	radiotherapy ; dose personalization ; head and neck cancer ; mathematical modeling ; Medicine ; R
Subject code	616
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ultra‐small superparamagnetic iron oxide (USPIO) magnetic resonance imaging in benign mixed tumor of the parotid gland

Jason M. Johnson / Abdallah S. R. Mohamed / Yao Ding / Jihong Wang / Stephen Y. Lai / Clifton D. Fuller / Rutvij Shah / Randall T. Butler / Randal S. Weber

Clinical Case Reports, Vol 9, Iss 1, Pp 123-

2021 Volume 127

Abstract: Abstract Historically USPIO has been used to help with nodal staging but not in primary tumors. The ability to concentrate USPIO may help to differentiate BMT from other types of parotid tumors. ...

Abstract	Abstract Historically USPIO has been used to help with nodal staging but not in primary tumors. The ability to concentrate USPIO may help to differentiate BMT from other types of parotid tumors.
Keywords	MRI ; parotid ; pleomorphic adenoma ; ultra‐small superparamagnetic iron oxide ; Medicine ; R ; Medicine (General) ; R5-920
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects

Kareem A. Wahid / Sara Ahmed / Renjie He / Lisanne V. van Dijk / Jonas Teuwen / Brigid A. McDonald / Vivian Salama / Abdallah S.R. Mohamed / Travis Salzillo / Cem Dede / Nicolette Taku / Stephen Y. Lai / Clifton D. Fuller / Mohamed A. Naser

Clinical and Translational Radiation Oncology, Vol 32, Iss , Pp 6-

Results from a prospective imaging registry

2022 Volume 14

Abstract: Background/Purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we ... ...

Abstract	Background/Purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. Materials/Methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers. Results: Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p < 0.05) and for FND for the T2 + Ve and ALL models (p < 0.05). No model demonstrated significant correlations between tumor size and DSC (p > 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p < 0.05), except those that included ADC or Ve as input channels (p > 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05). Conclusion: DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional ...
Keywords	Medical physics. Medical radiology. Nuclear medicine ; R895-920 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Subject code	669
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Three-Dimensional Evaluation of Isodose Radiation Volumes in Cases of Severe Mandibular Osteoradionecrosis for the Prediction of Recurrence after Segmental Resection

Haye H. Glas / Joep Kraeima / Silke Tribius / Frank K. J. Leusink / Carsten Rendenbach / Max Heiland / Carmen Stromberger / Ashkan Rashad / Clifton D. Fuller / Abdallah S. R. Mohamed / Stephen Y. Lai / Max J. H. Witjes

Journal of Personalized Medicine, Vol 12, Iss 834, p

2022 Volume 834

Abstract: Background: Pre-operative margin planning for the segmental resection of affected bone in mandibular osteoradionecrosis (ORN) is difficult. The aim of this study was to identify a possible relation between the received RT dose, exposed bone volume and ... ...

Abstract	Background: Pre-operative margin planning for the segmental resection of affected bone in mandibular osteoradionecrosis (ORN) is difficult. The aim of this study was to identify a possible relation between the received RT dose, exposed bone volume and the progression of ORN after segmental mandibular resection. Method: Patients diagnosed with grade 3-4 ORN for which a segmental resection was performed were included in the study. Three-dimensional reconstructions of RT isodose volumes were fused with postoperative imaging. The primary outcome was the recurrence of ORN after segmental resection. Subsequently, RT exposed mandibular bone volumes were calculated and the location of the bone cuts relative to the isodose volumes were assessed. Results: Five out of thirty-three patients developed recurrent ORN after segmental mandibular resection. All cases with recurrent ORN were resected inside an isodose volume of ≥56 Gy. The absolute mandibular volume radiated with 56 Gy was significantly smaller in the recurrent group (10.9 mL vs. 30.7 mL, p = 0.006), as was the proportion of the mandible radiated with 56 Gy (23% vs. 45%, p = 0.013). Conclusion: The volume of radiated bone was not predictive for risk of progression. The finding that recurrent ORN occurred with bone resection margins within the 56 Gy isodose volume suggests that this could serve as a starting point for the pre-operative planning of reducing the risk of ORN recurrence.
Keywords	osteoradionecrosis ; mandible ; radiotherapy ; surgery ; computer assisted surgery ; virtual surgical planning ; Medicine ; R
Subject code	616
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer

Kareem A. Wahid / Brennan Olson / Rishab Jain / Aaron J. Grossberg / Dina El-Habashy / Cem Dede / Vivian Salama / Moamen Abobakr / Abdallah S. R. Mohamed / Renjie He / Joel Jaskari / Jaakko Sahlsten / Kimmo Kaski / Clifton D. Fuller / Mohamed A. Naser

Scientific Data, Vol 9, Iss 1, Pp 1-

2022 Volume 6

Abstract: Measurement(s) skeletal muscle • adipose tissue Technology Type(s) computed ... ...

Abstract	Measurement(s) skeletal muscle • adipose tissue Technology Type(s) computed tomography
Keywords	Science ; Q
Language	English
Publishing date	2022-08-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction

Joel Tosado / Luka Zdilar / Hesham Elhalawani / Baher Elgohari / David M. Vock / G. Elisabeta Marai / Clifton Fuller / Abdallah S. R. Mohamed / Guadalupe Canahuate

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 14

Abstract: Abstract Clustering is the task of identifying groups of similar subjects according to certain criteria. The AJCC staging system can be thought as a clustering mechanism that groups patients based on their disease stage. This grouping drives prognosis ... ...

Abstract	Abstract Clustering is the task of identifying groups of similar subjects according to certain criteria. The AJCC staging system can be thought as a clustering mechanism that groups patients based on their disease stage. This grouping drives prognosis and influences treatment. The goal of this work is to evaluate the efficacy of machine learning algorithms to cluster the patients into discriminative groups to improve prognosis for overall survival (OS) and relapse free survival (RFS) outcomes. We apply clustering over a retrospectively collected data from 644 head and neck cancer patients including both clinical and radiomic features. In order to incorporate outcome information into the clustering process and deal with the large proportion of censored samples, the feature space was scaled using the regression coefficients fitted using a proxy dependent variable, martingale residuals, instead of follow-up time. Two clusters were identified and evaluated using cross validation. The Kaplan Meier (KM) curves between the two clusters differ significantly for OS and RFS (p-value < 0.0001). Moreover, there was a relative predictive improvement when using the cluster label in addition to the clinical features compared to using only clinical features where AUC increased by 5.7% and 13.0% for OS and RFS, respectively.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2020-03-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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