Manisha Palta

Overview:

Clinical research in gastrointestinal malignancies, lymphomas and breast malignancies.

Positions:

Associate Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 2007

University of Florida, College of Medicine

Intern, Internal Medicine

University of North Carolina - Chapel Hill

Resident, Radiation Oncology

Duke University School of Medicine

Grants:

PROCEED

Administered By
Radiation Oncology
Awarded By
Merck Sharp & Dohme
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

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

Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts.

Objectives: Expert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians. Materials and Methods: Two independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1. Results: The NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms. Conclusion: NLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.
Authors
Hong, JC; Fairchild, AT; Tanksley, JP; Palta, M; Tenenbaum, JD
MLA Citation
Hong, Julian C., et al. “Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts.Jamia Open, vol. 3, no. 4, Dec. 2020, pp. 513–17. Pubmed, doi:10.1093/jamiaopen/ooaa064.
URI
https://scholars.duke.edu/individual/pub1475026
PMID
33623888
Source
pubmed
Published In
Jamia Open
Volume
3
Published Date
Start Page
513
End Page
517
DOI
10.1093/jamiaopen/ooaa064

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment. PATIENTS AND METHODS: During this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots. RESULTS: Twice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, -10.0%; 95% CI, -18.3 to -1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851). CONCLUSION: In this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs.
Authors
Hong, JC; Eclov, NCW; Dalal, NH; Thomas, SM; Stephens, SJ; Malicki, M; Shields, S; Cobb, A; Mowery, YM; Niedzwiecki, D; Tenenbaum, JD; Palta, M
MLA Citation
URI
https://scholars.duke.edu/individual/pub1459314
PMID
32886536
Source
pubmed
Published In
Journal of Clinical Oncology
Volume
38
Published Date
Start Page
3652
End Page
3661
DOI
10.1200/JCO.20.01688

Fluence Map Prediction for Fast Pancreas Stereotactic Body Radiation Therapy (SBRT) Planning via Deep Learning

Authors
Wang, W; Sheng, Y; Palta, M; Czito, B; Willett, CG; Li, X; Wang, C; Zhang, J; Yin, FF; Wu, Q; Ge, Y; Wu, QJJ
MLA Citation
Wang, W., et al. “Fluence Map Prediction for Fast Pancreas Stereotactic Body Radiation Therapy (SBRT) Planning via Deep Learning.” International Journal of Radiation Oncology Biology Physics, vol. 108, no. 3, 2020, pp. S128–29.
URI
https://scholars.duke.edu/individual/pub1467597
Source
wos-lite
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
108
Published Date
Start Page
S128
End Page
S129