Adapting a machine learning algorithm to predict thyroid cytopathologyin lmic

Ended Project Grant Awarded Last action

Award ID
R21CA268428
Recipient
Awarding agency
Obligated
$977,717

Award facts

Assistance listing
93.393 Cancer Cause and Prevention Research
Completion date
Originally
Funding
  • $776,720.36 Federal
  • $200,997.00 Non-federal
Place of performance
Durham, NC 27710, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Pathology expertise and services in low and middle income countries (lmic) are severely inadequate and limited. One area in which pathology is especially critical for diagnosis and clinical decision making is thyroid related disease and cancers. The incidences of thyroid nodules and cancers have increased worldwide. A fundamental part of the clinical assessment for thyroid nodules is ultrasound-guided fine needle aspiration biopsy (fnab). Unfortunately, this cytopathology expertise is scarce in lmic and many nodules are surgically removed for diagnosis. Since 90-95% of these nodules are without malignancy, many patients undergo unnecessary surgery with the associated risks, financial costs, and stressing already limited resources. What is critically needed is a means to provide cytopathology expertise to interpret thyroid fnab in lmic. The overall goal of this proposal is to implement our ML approach into low resource settings to provide accurate and timely cytopathology analysis of fnab specimens. This R21/33 proposal focuses on MLA adaptation using smartphones for lmic (R21) and subsequent capacity building and implementation (R33) through well-established lmic research partnerships in tertiary centers in tanzania and vietnam. We hypothesize that our MLA can use smartphone captured images to assess probability of malignancy in thyroid fnab that is comparable to trained cytopathologists. Our aims are: R21: adaptation of a smartphone approach to capture cytopathology images for MLA analysis. Archived fnab samples used at different international institutions will be used to train and adapt the MLA with these images. Furthermore, we would train local personnel in lmic to image capture using this new setup. The results from this phase will be an adapted MLA using smartphone images that has comparable sensitivity and specificity to the original MLA. R33: implementation of the smartphone MLA approach into lmic clinical settings. A prospective implementation of this approach in tertiary centers in tanzania and vietnam will be conducted. Fnab samples obtained would undergo standard of care pathology assessment as well as be analyzed with the MLA protocol. Local cytopathologist assessment will be compared with the MLA analysis. Outcome measures include MLA assessment of slides, technical slide review and image capture, concordance with pathology between MLA and expertise both in US and lmic. The expected outcome will lead to a cost-effective method to implement MLA for thyroid fnab that can be used in lmic. This approach will not only assist in early diagnosis of thyroid cancers but also improve the utilization of limited resources through effectively identifying those that need surgery from those that do not.

Transactions

Action Federal Non-federal Date
Amendment -$1,504 $0
Amendment $1,000 $0
Amendment $186,615 $0
Amendment $186,615 $0
Amendment $200,997 $0
Amendment $1,000 $0
Amendment $1,000 $0
Original award $200,997 $200,997

Subawards

Subrecipient Amount Awarded
The Good Samaritan Foundation Founders Of The Kilimanjaro Christian Medical Centre Adapting a machine learning algorithm to predict thyroid cytopathologyin lmic $45,360
Florida Southern College At the end of R21 phase of the project, we trained and achieved a Machine Learning Algorithm (MLA) with an average accuracy above 90% on smartphone-captured… $48,758
University Of North Carolina At Chapel Hill The university of North Carolina at Chapel will collaborate with Prof. Walter Lee, the group at Duke University and team members of other sites involved in this… $45,878
Tel Aviv Sourasky Medical Center Our goal is to adapt our previously developed AI algorithm to the LMIC setting where the ROIs are manually selected. Our preliminary results suggest that ROIs… $58,172

Quick answers

Who received award R21CA268428?

R21CA268428 was awarded to Duke University by National Cancer Institute (National Institutes of Health) on August 1, 2021.

How much is award R21CA268428 worth?

$977,717 has been obligated on R21CA268428: $776,720 in federal funding and $200,997 in non-federal funding.

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