Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT05441098 |
Other study ID # |
NCC3299 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
April 1, 2022 |
Est. completion date |
December 31, 2022 |
Study information
Verified date |
June 2022 |
Source |
Chinese Academy of Medical Sciences |
Contact |
Jian Yue, M.D. |
Phone |
+86 18612621749 |
Email |
sunlight_1985[@]163.com |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Breast cancer is a major cause of survival for women worldwide. Neoadjuvant therapy as an
important treatment for locally advanced breast cancer has had many positive effects for
breast cancer patients. As drug therapy for breast cancer continues to evolve, the percentage
of pathologic complete responses continues to increase. However, at present, pCR can only be
judged by pathological testing of surgically resected specimens, and the question of whether
pCR can be accurately judged preoperatively has become an urgent issue.Therefore, this
project plans to establish and validate a model for determining pCR after NAT in breast
cancer based on clinical information, imaging and pathological information of breast cancer
patients in multiple centers using artificial intelligence technology in accordance with
international guidelines and domestic expert consensus on breast cancer NAT, in order to
solve the problem of surgical decision making for patients after NAT, by combining experts
from breast medicine, surgery, pathology and imaging departments in several tertiary care
hospitals across China. The model will be validated to solve the problem of surgical decision
making for post-NAT patients.
Description:
Breast cancer is the most prevalent cancer among women worldwide. Neoadjuvant treatment (NAT)
is part of the standardized treatment of breast cancer and is especially important for
locally advanced breast cancer. Numerous studies have shown that patients who achieve
pathological complete response (pCR) after NAT may have better disease-free and overall
survival rates, thus making the survival advantage of radical surgery less significant.
However, at present, pCR can only be judged by pathological testing of surgically resected
specimens, and the question of whether pCR can be accurately judged preoperatively has become
an urgent issue.
Artificial intelligence (AI) technology is a branch of computer science that attempts to
understand the essence of intelligence and produce a new intelligent machine that can respond
in a similar way to human intelligence, of which image recognition is widely used in clinical
research as an important component of AI. In recent years, with the development of AI and
related algorithms, more and more researchers are working on the use of image images to
determine the efficacy of NAT more precisely, trying to exempt a fraction of patients who
achieve pCR from radical surgery and never achieve a better appearance and quality of
survival.
Simon's team selected 246 patients who attended Salzburg Oncology Center in Australia from
2006-2016, had pre-surgical DCE-MRI read by imaging scientists with more than 10 years of
experience, and gave a judgment of complete remission, only to obtain a more pessimistic
result: a positive predictive value of only 48%. jinsun's team selected patients who
underwent NAT at Samsung Medical Center from 2007-2016. The results showed that the kappa
value of the concordance test between radiologic complete response (rCR) and breast pCR was
0.459, and the kappa value of the concordance test between axillary rCR and axillary pCR
Woo's and Erika's teams analyzed the subgroups that led to false-negative MRI determinations
of pCR and suggested that patients with G1-2, Luminal A/B subtypes, and non-lumpy enhancement
had difficulty assessing complete remission by MRI. It is evident that it is now difficult to
use traditional modeling approaches for pCR to determine the level of clinical application.
Elizabeth's team used preoperative MRI, AI technology for feature extraction, and
clinicopathological information to construct a pCR determination model, which performed well
in the independent validation set with an AUC of 0.83 (95% CI: 0.71-0.94). Imon's team used
Riesz feature extraction to determine pCR in triple-negative breast cancer patients
undergoing NAT, and the final model ROC reached 0.85. Professor Yang Fan's team from the
Department of Radiology, Wuhan Union Medical College Hospital, Wuhan, China, used multi-phase
DCE-MRI parameters and machine learning algorithms to build the model, and the highest ROC
area under the curve reached 0.919. The above results show that the pCR determination model
of imaging histology with AI technology is a big improvement compared with the traditional
model.
Although an increasing number of studies have confirmed the importance of imaging histology
for NAT pCR determination, many of the current studies have some flaws. When evaluated using
the international standard imaging histology RQS score, it was found that most of the
studies: (i) lacked external validation cohorts; (ii) did not provide appropriate
descriptions of parameter extraction; (iii) lacked a standardized process for imaging
parameters; and (iv) had small sample sizes. Therefore more systematic and standardized
studies are yet to be carried out by the majority of researchers.
Therefore, this project plans to establish and validate a model for determining pCR after NAT
in breast cancer based on clinical information, imaging and pathological information of
breast cancer patients in multiple centers using artificial intelligence technology in
accordance with international guidelines and domestic expert consensus on breast cancer NAT,
in order to solve the problem of surgical decision making for patients after NAT, by
combining experts from breast medicine, surgery, pathology and imaging departments in several
tertiary care hospitals across China. The model will be validated to solve the problem of
surgical decision making for post-NAT patients.