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 V33Gy), 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.
Authors
Wang, W; Sheng, Y; Palta, M; Czito, B; Willett, C; Hito, M; Yin, F-F; Wu, Q; Ge, Y; Wu, QJ
MLA Citation
Wang, Wentao, et al. “Deep Learning-Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost.Adv Radiat Oncol, vol. 6, no. 4, July 2021, p. 100672. Pubmed, doi:10.1016/j.adro.2021.100672.
URI
https://scholars.duke.edu/individual/pub1481730
PMID
33997484
Source
pubmed
Published In
Advances in Radiation Oncology
Volume
6
Published Date
Start Page
100672
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

Brain Metastases from Esophageal Squamous Cell Carcinoma: Clinical Characteristics and Prognosis.

<h4>Purpose</h4>Due to the low incidence of intracranial disease among patients with esophageal cancer (EC), optimal management for these patients has not been established. The aim of this real-world study is to describe the clinical characteristics, treatment approaches, and outcomes for esophageal squamous cell carcinoma (ESCC) patients with brain metastases in order to provide a reference for treatment and associated outcomes of these patients.<h4>Methods</h4>Patients with ESCC treated at the Fourth Hospital of Hebei Medical University between January 1, 2009 and May 31,2020 were identified in an institutional tumor registry. Patients with brain metastases were included for further analysis and categorized by treatment received. Survival was evaluated by the Kaplan-Meier method and Cox proportional hazards models.<h4>Results</h4>Among 19,225 patients with ESCC, 66 (0.34%) were diagnosed with brain metastases. Five patients were treated with surgery, 40 patients were treated with radiotherapy, 10 with systemic therapy alone, and 15 with supportive care alone. The median follow-up time was 7.3 months (95% CI 7.4-11.4). At last follow-up, 59 patients are deceased and 7 patients are alive. Median overall survival (OS) from time of brain metastases diagnosis was 7.6 months (95% CI 5.3-9.9) for all cases. For patients who received locoregional treatment, median OS was 10.9 months (95% CI 7.4-14.3), and survival rates at 6 and 12 months were 75.6% and 37.2%, respectively. For patients without locoregional treatment, median OS was 3.0 months (95% CI 2.5-3.5), and survival rates at 6 and 12 months were 32% and 24%, respectively. OS was significantly improved for patients who received locoregional treatment compared to those treated with systematic treatment alone or supportive care (HR: 2.761, 95% CI 1.509-5.053, P=0.001). The median OS of patients with diagnosis-specific graded prognostic assessment (DS-GPA) score 0-2 was 6.4 months, compared to median OS of 12.3 months for patients with DS-GPA >2 (HR: 0.507, 95% CI 0.283-0.911).<h4>Conclusion</h4>Brain metastases are rare in patients with ESCC. DS-GPA score maybe a useful prognostic tool for ESCC patients with brain metastases. Receipt of locoregional treatment including brain surgery and radiotherapy was associated with improved survival.
Authors
Xiao, L; Mowery, YM; Czito, BG; Wu, Y; Gao, G; Zhai, C; Wang, J; Wang, J
MLA Citation
Xiao, Linlin, et al. “Brain Metastases from Esophageal Squamous Cell Carcinoma: Clinical Characteristics and Prognosis.Frontiers in Oncology, vol. 11, 2021, p. 652509. Epmc, doi:10.3389/fonc.2021.652509.
URI
https://scholars.duke.edu/individual/pub1476234
PMID
33996573
Source
epmc
Published In
Frontiers in Oncology
Volume
11
Published Date
Start Page
652509
DOI
10.3389/fonc.2021.652509

Randomized, Double-Blinded, Placebo-controlled Multicenter Adaptive Phase 1-2 Trial of GC 4419, a Dismutase Mimetic, in Combination with High Dose Stereotactic Body Radiation Therapy (SBRT) in Locally Advanced Pancreatic Cancer (PC).

Authors
Hoffe, S; Frakes, JM; Aguilera, TA; Czito, B; Palta, M; Brookes, M; Schweizer, C; Colbert, L; Moningi, S; Bhutani, MS; Pant, S; Tzeng, CW; Tidwell, RS; Thall, P; Yuan, Y; Moser, EC; Holmlund, J; Herman, J; Taniguchi, CM
URI
https://scholars.duke.edu/individual/pub1470085
PMID
33427657
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
108
Published Date
Start Page
1399
End Page
1400
DOI
10.1016/j.ijrobp.2020.09.022

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence maps from patient anatomy using convolutional neural networks (CNNs). Materials and Methods: Our proposed framework utilizes two CNNs to predict intensity-modulated radiation therapy fluence maps and generate deliverable plans: (1) Field-dose CNN predicts field-dose distributions in the region of interest using planning images and structure contours; (2) a fluence map CNN predicts the final fluence map per beam using the predicted field dose projected onto the beam's eye view. The predicted fluence maps were subsequently imported into the treatment planning system for leaf sequencing and final dose calculation (model-predicted plans). One hundred patients previously treated with pancreas SBRT were included in this retrospective study, and they were split into 85 training cases and 15 test cases. For each network, 10% of training data were randomly selected for model validation. Nine-beam benchmark plans with standardized target prescription and organ-at-risk constraints were planned by experienced clinical physicists and used as the gold standard to train the model. Model-predicted plans were compared with benchmark plans in terms of dosimetric endpoints, fluence map deliverability, and total monitor units. Results: The average time for fluence-map prediction per patient was 7.1 s. Comparing model-predicted plans with benchmark plans, target mean dose, maximum dose (0.1 cc), and D95% absolute differences in percentages of prescription were 0.1, 3.9, and 2.1%, respectively; organ-at-risk mean dose and maximum dose (0.1 cc) absolute differences were 0.2 and 4.4%, respectively. The predicted plans had fluence map gamma indices (97.69 ± 0.96% vs. 98.14 ± 0.74%) and total monitor units (2,122 ± 281 vs. 2,265 ± 373) that were comparable to the benchmark plans. Conclusions: We develop a novel deep learning framework for pancreas SBRT planning, which predicts a fluence map for each beam and can, therefore, bypass the lengthy inverse optimization process. The proposed framework could potentially change the paradigm of treatment planning by harnessing the power of deep learning to generate clinically deliverable plans in seconds.
Authors
Wang, W; Sheng, Y; Wang, C; Zhang, J; Li, X; Palta, M; Czito, B; Willett, CG; Wu, Q; Ge, Y; Yin, F-F; Wu, QJ
MLA Citation
Wang, Wentao, et al. “Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.Front Artif Intell, vol. 3, 2020, p. 68. Pubmed, doi:10.3389/frai.2020.00068.
URI
https://scholars.duke.edu/individual/pub1476685
PMID
33733185
Source
pubmed
Published In
Front Artif Intell
Volume
3
Published Date
Start Page
68
DOI
10.3389/frai.2020.00068