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Article ; Online: Chemotherapy Only For High-risk Features.

Yang, George Y / Caudell, Jimmy J

International journal of radiation oncology, biology, physics

2022 Volume 114, Issue 1, Page(s) 13

MeSH term(s)	Chemotherapy, Adjuvant ; Humans ; Neoadjuvant Therapy
Language	English
Publishing date	2022-08-09
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	197614-x
ISSN	1879-355X ; 0360-3016
ISSN (online)	1879-355X
ISSN	0360-3016
DOI	10.1016/j.ijrobp.2022.05.032
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Article ; Online: Technical feasibility of novel immunostimulatory low-dose radiation for polymetastatic disease with CBCT-based online adaptive and conventional approaches.

Nasser, Nour / Perez, Bradford A / Penagaricano, Jose A / Caudell, Jimmy J / Oliver, Daniel E / Latifi, Kujtim / Moros, Eduardo G / Redler, Gage

Journal of applied clinical medical physics

2024 , Page(s) e14303

Abstract: Purpose: A workflow/planning strategy delivering low-dose radiation therapy (LDRT) (1 Gy) to all polymetastatic diseases using conventional planning/delivery (Raystation/Halcyon = "conventional") and the AI-based Ethos online adaptive RT (oART) platform ...

Abstract	Purpose: A workflow/planning strategy delivering low-dose radiation therapy (LDRT) (1 Gy) to all polymetastatic diseases using conventional planning/delivery (Raystation/Halcyon = "conventional") and the AI-based Ethos online adaptive RT (oART) platform is developed/evaluated. Methods: Using retrospective data for ten polymetastatic non-small cell lung cancer patients (5-52 lesions each) with PET/CTs, gross tumor volumes (GTVs) were delineated using PET standardized-uptake-value (SUV) thresholding. A 1 cm uniform expansion of GTVs to account for setup/contour uncertainty and organ motion-generated planning target volumes (PTVs). Dose optimization/calculation used the diagnostic CT from PET/CT. Dosimetric objectives were: D Results: All initial plans generated, both for Raystation and Ethos, achieved clinical goals within acceptable variation. For all patients, D Conclusions: This study demonstrates feasibility of conventional planning/treatment with Raystation/Halcyon and highlights efficiency gains when utilizing semi-automated planning/online-adaptive treatment with Ethos for immunostimulatory LDRT conformally delivered to all sites of polymetastatic disease.
Language	English
Publishing date	2024-02-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2010347-5
ISSN	1526-9914 ; 1526-9914
ISSN (online)	1526-9914
ISSN	1526-9914
DOI	10.1002/acm2.14303
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Article ; Online: Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer.

Dickey, Brittney L / Putney, Ryan M / Schell, Michael J / Berglund, Anders E / Amelio, Antonio L / Caudell, Jimmy J / Chung, Christine H / Giuliano, Anna R

Cancer prevention research (Philadelphia, Pa.)

2024 Volume 17, Issue 4, Page(s) 169–176

Abstract: As oropharyngeal cancer (OPC) associated with human papillomavirus (HPV) increases in men, the need for a screening test to diagnose OPC early is crucial. This study agnostically identified differentially methylated CpG sites to identify additional ... ...

Abstract	As oropharyngeal cancer (OPC) associated with human papillomavirus (HPV) increases in men, the need for a screening test to diagnose OPC early is crucial. This study agnostically identified differentially methylated CpG sites to identify additional biomarkers to improve screening for early OPC.DNA was extracted from oral gargles of 89 early cases and 108 frequency matched healthy controls, and processed for genome-wide methylation using the Illumina Infinium MethylationEPIC BeadChip. Selected sites were combined with our prior methylation data in the EPB41L3 gene (CpG sites 438, 427, and 425) and oral HPV16 and HPV18 status were considered as binary variables (positive/negative). Lasso regression identified CpG sites strongly associated with early OPC. ROC curves with AUC were generated. The panel was validated utilizing bootstrap resampling.Machine learning analyses identified 14 markers that are significantly associated with early OPC, including one EPB41L3 CpG site (438) and oral HPV16 status. A final model was trained on all available samples using the discovered panel and was able to predict early OPC compared with controls with an AUC of 0.970 on the training set. In the bootstrap validation sets, the average AUC was 0.935, indicating adequate internal validity.Our data suggest that this panel can detect OPC early, however external validation of this panel is needed. Further refinement of a panel of biomarkers to diagnose OPC earlier is urgently needed to prevent complex treatment of OPC and associated comorbidities, while reducing risk of recurrence. Prevention relevance: This study identified biomarkers using genome-wide methylation to create a panel capable of discerning early oropharyngeal cancer (OPC) from those without OPC. Such a biomarker panel would be an effective tool to detect OPC early and prevent complications of treatment associated with later diagnosis.
MeSH term(s)	Male ; Humans ; Human Papillomavirus Viruses ; Papillomavirus Infections/complications ; Papillomavirus Infections/diagnosis ; Oropharyngeal Neoplasms/diagnosis ; Oropharyngeal Neoplasms/genetics ; Biomarkers, Tumor/genetics ; Human papillomavirus 16/genetics ; Methylation ; Microfilament Proteins
Chemical Substances	Biomarkers, Tumor ; EPB41L3 protein, human ; Microfilament Proteins
Language	English
Publishing date	2024-01-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2434717-6
ISSN	1940-6215 ; 1940-6207
ISSN (online)	1940-6215
ISSN	1940-6207
DOI	10.1158/1940-6207.CAPR-23-0317
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Article ; Online: Modelling Radiation Cancer Treatment with a Death-Rate Term in Ordinary and Fractional Differential Equations.

Wilson, Nicole / Drapaca, Corina S / Enderling, Heiko / Caudell, Jimmy J / Wilkie, Kathleen P

Bulletin of mathematical biology

2023 Volume 85, Issue 6, Page(s) 47

Abstract: Fractional calculus has recently been applied to the mathematical modelling of tumour growth, but its use introduces complexities that may not be warranted. Mathematical modelling with differential equations is a standard approach to study and predict ... ...

Abstract	Fractional calculus has recently been applied to the mathematical modelling of tumour growth, but its use introduces complexities that may not be warranted. Mathematical modelling with differential equations is a standard approach to study and predict treatment outcomes for population-level and patient-specific responses. Here, we use patient data of radiation-treated tumours to discuss the benefits and limitations of introducing fractional derivatives into three standard models of tumour growth. The fractional derivative introduces a history-dependence into the growth function, which requires a continuous death-rate term for radiation treatment. This newly proposed radiation-induced death-rate term improves computational efficiency in both ordinary and fractional derivative models. This computational speed-up will benefit common simulation tasks such as model parameterization and the construction and running of virtual clinical trials.
MeSH term(s)	Humans ; Models, Biological ; Mathematical Concepts ; Neoplasms/radiotherapy ; Models, Theoretical ; Computer Simulation
Language	English
Publishing date	2023-04-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	184905-0
ISSN	1522-9602 ; 0007-4985 ; 0092-8240
ISSN (online)	1522-9602
ISSN	0007-4985 ; 0092-8240
DOI	10.1007/s11538-023-01139-2
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Zs.B 754: Show issues

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Article: Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control.

Kutuva, Achyudhan R / Caudell, Jimmy J / Yamoah, Kosj / Enderling, Heiko / Zahid, Mohammad U

Frontiers in oncology

2023 Volume 13, Page(s) 1130966

Abstract: Introduction: Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying ... ...

Abstract	Introduction: Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose. Methods: In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values. Results: Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC. Discussion: Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.
Language	English
Publishing date	2023-10-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2023.1130966
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Article ; Online: A head and neck treatment planning strategy for a CBCT-guided ring-gantry online adaptive radiotherapy system.

Nasser, Nour / Yang, George Q / Koo, Jihye / Bowers, Mark / Greco, Kevin / Feygelman, Vladimir / Moros, Eduardo G / Caudell, Jimmy J / Redler, Gage

Journal of applied clinical medical physics

2023 Volume 24, Issue 12, Page(s) e14134

Abstract: Purpose: A planning strategy was developed and the utility of online-adaptation with the Ethos CBCT-guided ring-gantry adaptive radiotherapy (ART) system was evaluated using retrospective data from Head-and-neck (H&N) patients that required clinical ... ...

Abstract	Purpose: A planning strategy was developed and the utility of online-adaptation with the Ethos CBCT-guided ring-gantry adaptive radiotherapy (ART) system was evaluated using retrospective data from Head-and-neck (H&N) patients that required clinical offline adaptation during treatment. Methods: Clinical data were used to re-plan 20 H&N patients (10 sequential boost (SEQ) with separate base and boost plans plus 10 simultaneous integrated boost (SIB)). An optimal approach, robust to online adaptation, for Ethos-initial plans using clinical goal prioritization was developed. Anatomically-derived isodose-shaping helper structures, air-density override, goals for controlling hotspot location(s), and plan normalization were investigated. Online adaptation was simulated using clinical offline adaptive simulation-CTs to represent an on-treatment CBCT. Dosimetric comparisons were based on institutional guidelines for Clinical-initial versus Ethos-initial plans and Ethos-scheduled versus Ethos-adapted plans. Timing for five components of the online adaptive workflow was analyzed. Results: The Ethos H&N planning approach generated Ethos-initial SEQ plans with clinically comparable PTV coverage (average PTV Conclusions: With an optimal planning approach, Ethos efficiently auto-generated dosimetrically comparable and clinically acceptable initial SEQ plans for H&N patients. Initial SIB plans were inferior and clinically unacceptable, but adapted SIB plans became clinically acceptable. Online adapted plans optimized dose to new anatomy and maintained target coverage/homogeneity with improved OAR sparing in a time-efficient manner.
MeSH term(s)	Humans ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted ; Retrospective Studies ; Spiral Cone-Beam Computed Tomography ; Organs at Risk ; Radiotherapy, Intensity-Modulated
Language	English
Publishing date	2023-08-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	2010347-5
ISSN	1526-9914 ; 1526-9914
ISSN (online)	1526-9914
ISSN	1526-9914
DOI	10.1002/acm2.14134
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Article ; Online: More Information Needed, Surgery Provides It.

Otto, Kristen J / Caudell, Jimmy J

International journal of radiation oncology, biology, physics

2017 Volume 99, Issue 5, Page(s) 1061–1062

Language	English
Publishing date	2017-12-01
Publishing country	United States
Document type	Letter
ZDB-ID	197614-x
ISSN	1879-355X ; 0360-3016
ISSN (online)	1879-355X
ISSN	0360-3016
DOI	10.1016/j.ijrobp.2017.08.016
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Article ; Online: Challenges With the 8th Edition of the AJCC Cancer Staging Manual for Breast, Testicular, and Head and Neck Cancers.

Sahin, Aysegul A / Gilligan, Timothy D / Caudell, Jimmy J

Journal of the National Comprehensive Cancer Network : JNCCN

2019 Volume 17, Issue 5.5, Page(s) 560–564

Abstract: Three experts discussed changes in the 8th edition of the AJCC Cancer Staging Manual and challenges regarding these changes for staging of breast cancer, testicular cancer, and head and neck cancer, respectively. In general, the staging changes for ... ...

Abstract	Three experts discussed changes in the 8th edition of the AJCC Cancer Staging Manual and challenges regarding these changes for staging of breast cancer, testicular cancer, and head and neck cancer, respectively. In general, the staging changes for breast cancer and for human papillomavirus-positive oropharyngeal cancer were hailed as improvements, but the changes for testicular cancer were questioned as to their clinical relevance. Better studies are needed to improve staging for human papillomavirus-negative oropharyngeal cancer.
MeSH term(s)	Breast Neoplasms/diagnosis ; Female ; Head and Neck Neoplasms/diagnosis ; Humans ; Male ; Neoplasm Staging/methods ; Neoplasm Staging/standards ; Practice Guidelines as Topic ; Testicular Neoplasms/diagnosis
Language	English
Publishing date	2019-05-21
Publishing country	United States
Document type	Letter
ZDB-ID	2250759-0
ISSN	1540-1413 ; 1540-1405
ISSN (online)	1540-1413
ISSN	1540-1405
DOI	10.6004/jnccn.2019.5015
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Article ; Online: Maximizing Cures and Preserving Function in Head and Neck Cancers.

Harrison, Louis B / Caudell, Jimmy J

Cancer control : journal of the Moffitt Cancer Center

2016 Volume 23, Issue 3, Page(s) 192

MeSH term(s)	Head and Neck Neoplasms ; Humans
Language	English
Publishing date	2016-07
Publishing country	United States
Document type	Editorial
ZDB-ID	1328503-8
ISSN	1526-2359 ; 1073-2748
ISSN (online)	1526-2359
ISSN	1073-2748
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Article ; Online: Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy.

Koo, Jihye / Caudell, Jimmy J / Latifi, Kujtim / Jordan, Petr / Shen, Sangyu / Adamson, Philip M / Moros, Eduardo G / Feygelman, Vladimir

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

2022 Volume 174, Page(s) 52–58

Abstract: Purpose: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm.: Methods: A total of 864 HN cancer ... ...

Abstract	Purpose: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm. Methods: A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases. Results: Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful. Conclusion: The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.
MeSH term(s)	Algorithms ; Deep Learning ; Head and Neck Neoplasms/radiotherapy ; Humans ; Organs at Risk ; Radiotherapy Planning, Computer-Assisted/methods ; Reproducibility of Results
Language	English
Publishing date	2022-07-09
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	605646-5
ISSN	1879-0887 ; 0167-8140
ISSN (online)	1879-0887
ISSN	0167-8140
DOI	10.1016/j.radonc.2022.06.024
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