Brian Czito

Overview:

Listed in Best Doctors in America. Listed in Top Doctors in North Carolina. His research interests include gastrointestinal malignancies, including treatment and integration of novel systemic agents with radiation therapy in the treatment of esophageal, gastric, hepatobiliary, pancreatic, colorectal and anal malignancies; phase I/II clinical trials evaluating novel systemic/targeted agents in conjunction with radiation therapy; investigation and optimization of the treatment of gastrointestinal malignancies, with focus on the above tumor sites.

Positions:

Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 1996

Medical College of Georgia School of Medicine

Intern

St. Joseph Mercy Health Systems

Resident

Massachusetts General Hospital

Chief Resident

Massachusetts General Hospital

American Board of Radiology (ABR)

American Board of Radiology

Grants:

Phase II Randomized Trial comparing Percutaneous Ablation to Hypofractionaed Image Guided Radiation Therapy in Veteran and Non-Veteran, Non-surgical Hepatocelluar Carcinoma Patients (PROVE-HCC)

Administered By
Radiation Oncology
Awarded By
Varian Medical Systems, Inc.
Role
Principal Investigator
Start Date
End Date

AN ADAPTIVE PHASE I/II DOSE ESCALATION TRIAL OF STEREOTACTIC BODY RADIATION THERAPY IN COMBINATION WITH RADIOMODULATING AGENT GC4419 IN LOCALLY ADVANCED PANCREATIC ADENOCARCINOMA

Administered By
Radiation Oncology
Awarded By
Galera Therapeutics, Inc.
Role
Principal Investigator
Start Date
End Date

Publications:

Deep Learning–Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a challenging task, especially with simultaneous integrated boost treatment approaches. We propose a deep learning (DL) framework to accurately predict fluence maps from patient anatomy and directly generate intensity modulated radiation therapy plans. Methods and Materials: The framework employs 2 convolutional neural networks (CNNs) to sequentially generate beam dose prediction and fluence map prediction, creating a deliverable 9-beam intensity modulated radiation therapy plan. Within the beam dose prediction CNN, axial slices of combined structure contour masks are used to predict 3-dimensional (3D) beam doses for each beam. Each 3D beam dose is projected along its beam's-eye-view to form a 2D beam dose map, which is subsequently used by the fluence map prediction CNN to predict its fluence map. Finally, the 9 predicted fluence maps are imported into the treatment planning system to finalize the plan by leaf sequencing and dose calculation. One hundred patients receiving pancreas SBRT were retrospectively collected for this study. Benchmark plans with unified simultaneous integrated boost prescription (25/33 Gy) were manually optimized for each case. The data set was split into 80/20 cases for training and testing. We evaluated the proposed DL framework by assessing both the fluence maps and the final predicted plans. Further, clinical acceptability of the plans was evaluated by a physician specializing in gastrointestinal cancer. Results: The DL-based planning was, on average, completed in under 2 minutes. In testing, the predicted plans achieved similar dose distribution compared with the benchmark plans (-1.5% deviation for planning target volume 33 V ), with slightly higher planning target volume maximum (+1.03 Gy) and organ at risk maximum (+0.95 Gy) doses. After renormalization, the physician rated 19 cases clinically acceptable and 1 case requiring minor improvement. Conclusions: The DL framework can effectively plan pancreas SBRT cases within 2 minutes. The predicted plans are clinically deliverable, with plan quality approaching that of manual planning. 33Gy
Authors
Wang, W; Sheng, Y; Palta, M; Czito, B; Willett, C; Hito, M; Yin, FF; Wu, Q; Ge, Y; Wu, QJ
MLA Citation
Wang, W., et al. “Deep Learning–Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost (Accepted).” Advances in Radiation Oncology, vol. 6, no. 4, July 2021. Scopus, doi:10.1016/j.adro.2021.100672.
URI
https://scholars.duke.edu/individual/pub1481730
Source
scopus
Published In
Advances in Radiation Oncology
Volume
6
Published Date
DOI
10.1016/j.adro.2021.100672

Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology.

Pancreatic cancer is the fourth leading cause of cancer-related death among men and women in the United States. A major challenge in treatment remains patients' advanced disease at diagnosis. The NCCN Guidelines for Pancreatic Adenocarcinoma provides recommendations for the diagnosis, evaluation, treatment, and follow-up for patients with pancreatic cancer. Although survival rates remain relatively unchanged, newer modalities of treatment, including targeted therapies, provide hope for improving patient outcomes. Sections of the manuscript have been updated to be concordant with the most recent update to the guidelines. This manuscript focuses on the available systemic therapy approaches, specifically the treatment options for locally advanced and metastatic disease.
Authors
Tempero, MA; Malafa, MP; Al-Hawary, M; Behrman, SW; Benson, AB; Cardin, DB; Chiorean, EG; Chung, V; Czito, B; Del Chiaro, M; Dillhoff, M; Donahue, TR; Dotan, E; Ferrone, CR; Fountzilas, C; Hardacre, J; Hawkins, WG; Klute, K; Ko, AH; Kunstman, JW; LoConte, N; Lowy, AM; Moravek, C; Nakakura, EK; Narang, AK; Obando, J; Polanco, PM; Reddy, S; Reyngold, M; Scaife, C; Shen, J; Vollmer, C; Wolff, RA; Wolpin, BM; Lynn, B; George, GV
MLA Citation
Tempero, Margaret A., et al. “Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology.J Natl Compr Canc Netw, vol. 19, no. 4, Apr. 2021, pp. 439–57. Pubmed, doi:10.6004/jnccn.2021.0017.
URI
https://scholars.duke.edu/individual/pub1480619
PMID
33845462
Source
pubmed
Published In
J Natl Compr Canc Netw
Volume
19
Published Date
Start Page
439
End Page
457
DOI
10.6004/jnccn.2021.0017

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

PURPOSE: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. METHODS AND MATERIALS: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set. RESULTS: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process. CONCLUSIONS: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies.
Authors
MLA Citation
Zhang, Jiahan, et al. “An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.Int J Radiat Oncol Biol Phys, vol. 109, no. 4, Mar. 2021, pp. 1076–85. Pubmed, doi:10.1016/j.ijrobp.2020.10.019.
URI
https://scholars.duke.edu/individual/pub1464387
PMID
33115686
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
109
Published Date
Start Page
1076
End Page
1085
DOI
10.1016/j.ijrobp.2020.10.019

Ipilimumab and Radiation in Patients with High-risk Resected or Regionally Advanced Melanoma.

PURPOSE: In this prospective trial, we sought to assess the feasibility of concurrent administration of ipilimumab and radiation as adjuvant, neoadjuvant, or definitive therapy in patients with regionally advanced melanoma. PATIENTS AND METHODS: Twenty-four patients in two cohorts were enrolled and received ipilimumab at 3 mg/kg every 3 weeks for four doses in conjunction with radiation; median dose was 4,000 cGy (interquartile range, 3,550-4,800 cGy). Patients in cohort 1 were treated adjuvantly; patients in cohort 2 were treated either neoadjuvantly or as definitive therapy. RESULTS: Adverse event profiles were consistent with those previously reported with checkpoint inhibition and radiation. For the neoadjuvant/definitive cohort, the objective response rate was 64% (80% confidence interval, 40%-83%), with 4 of 10 evaluable patients achieving a radiographic complete response. An additional 3 patients in this cohort had a partial response and went on to surgical resection. With 2 years of follow-up, the 6-, 12-, and 24-month relapse-free survival for the adjuvant cohort was 85%, 69%, and 62%, respectively. At 2 years, all patients in the neoadjuvant/definitive cohort and 10/13 patients in the adjuvant cohort were still alive. Correlative studies suggested that response in some patients were associated with specific CD4+ T-cell subsets. CONCLUSIONS: Overall, concurrent administration of ipilimumab and radiation was feasible, and resulted in a high response rate, converting some patients with unresectable disease into surgical candidates. Additional studies to investigate the combination of radiation and checkpoint inhibitor therapy are warranted.
Authors
Salama, AKS; Palta, M; Rushing, CN; Selim, MA; Linney, KN; Czito, BG; Yoo, DS; Hanks, BA; Beasley, GM; Mosca, PJ; Dumbauld, C; Steadman, KN; Yi, JS; Weinhold, KJ; Tyler, DS; Lee, WT; Brizel, DM
MLA Citation
Salama, April K. S., et al. “Ipilimumab and Radiation in Patients with High-risk Resected or Regionally Advanced Melanoma.Clin Cancer Res, vol. 27, no. 5, Mar. 2021, pp. 1287–95. Pubmed, doi:10.1158/1078-0432.CCR-20-2452.
URI
https://scholars.duke.edu/individual/pub1464016
PMID
33172894
Source
pubmed
Published In
Clinical Cancer Research
Volume
27
Published Date
Start Page
1287
End Page
1295
DOI
10.1158/1078-0432.CCR-20-2452

Emerging Treatment Paradigms in Localized Rectal Cancer.

Authors
Czito, B; Rödel, C
MLA Citation
Czito, Brian, and Claus Rödel. “Emerging Treatment Paradigms in Localized Rectal Cancer.Pract Radiat Oncol, vol. 11, no. 1, Jan. 2021, pp. 26–29. Pubmed, doi:10.1016/j.prro.2020.11.004.
URI
https://scholars.duke.edu/individual/pub1469159
PMID
33390242
Source
pubmed
Published In
Pract Radiat Oncol
Volume
11
Published Date
Start Page
26
End Page
29
DOI
10.1016/j.prro.2020.11.004