PMID- 37245194
OWN - NLM
STAT- MEDLINE
DCOM- 20230907
LR  - 20230907
IS  - 2662-4737 (Electronic)
IS  - 2662-4729 (Print)
IS  - 2662-4729 (Linking)
VI  - 46
IP  - 3
DP  - 2023 Sep
TI  - Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy 
      inference system (ANFIS).
PG  - 1071-1080
LID - 10.1007/s13246-023-01276-x [doi]
AB  - In chest computed tomography (CT), the breasts located within the scan range 
      receive a substantial radiation dose. Due to the risk of breast-related 
      carcinogenesis, analyzing the breast dose for justification of CT examinations 
      seems necessary. The main goal of this study is to overcome the limitations of 
      conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by 
      introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this 
      study, the breast dose of 50 adult female patients who underwent chest CT 
      examinations was measured directly by TLDs. Then, the ANFIS model was developed 
      with four inputs including dose length product (DLP), volumetric CT dose index 
      (CTDI(vol)), total mAs, and size-specific dose estimate (SSDE), and one output 
      (TLD dose). Additionally, multiple linear regression (MLR) as a traditional 
      prediction model was used for linear modeling and its results were compared with 
      the ANFIS. The TLD reader results showed that the breast dose value was 
      12.37 +/- 2.46 mGy. Performance indices of the ANFIS model, including root mean 
      square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 
      0.93 for the testing dataset, respectively. Also, the ANFIS model had superior 
      performance in predicting the breast dose than the MLR model (R = 0.805). This 
      study demonstrates that the proposed ANFIS model is efficient for patient dose 
      prediction in CT scans. Therefore, intelligence models such as ANFIS are 
      suggested to estimate and optimize patient dose in CT examinations.
CI  - (c) 2023. Australasian College of Physical Scientists and Engineers in Medicine.
FAU - Bahonar, Bahareh Moradmand
AU  - Bahonar BM
AUID- ORCID: 0000-0002-2516-2765
AD  - Department of Radiology and Radiotherapy Technology, Tehran University of Medical 
      Sciences, Tehran, Iran.
FAU - Changizi, Vahid
AU  - Changizi V
AUID- ORCID: 0000-0003-2015-1642
AD  - Department of Radiology and Radiotherapy Technology, Tehran University of Medical 
      Sciences, Tehran, Iran. changizi@sina.tums.ac.ir.
FAU - Ebrahiminia, Ali
AU  - Ebrahiminia A
AD  - Department of Medical Physics, School of Medicine, Guilan University of Medical 
      Sciences, Rasht, Iran.
FAU - Baradaran, Samaneh
AU  - Baradaran S
AD  - Nuclear Science and Technology Research Institute, Tehran, Iran.
LA  - eng
GR  - 54042/Tehran University of Medical Sciences and Health Services/
PT  - Journal Article
DEP - 20230528
PL  - Switzerland
TA  - Phys Eng Sci Med
JT  - Physical and engineering sciences in medicine
JID - 101760671
SB  - IM
MH  - Adult
MH  - Humans
MH  - Female
MH  - *Fuzzy Logic
MH  - Linear Models
MH  - *Tomography, X-Ray Computed
PMC - PMC10225119
OTO - NOTNLM
OT  - ANFIS
OT  - Breast dose
OT  - CT
OT  - Radiation dosimetry
OT  - TLD
COIS- The authors have no relevant financial or non-financial interests to disclose.
EDAT- 2023/05/28 13:09
MHDA- 2023/09/07 06:42
PMCR- 2023/05/28
CRDT- 2023/05/28 11:05
PHST- 2022/08/21 00:00 [received]
PHST- 2023/05/05 00:00 [accepted]
PHST- 2023/09/07 06:42 [medline]
PHST- 2023/05/28 13:09 [pubmed]
PHST- 2023/05/28 11:05 [entrez]
PHST- 2023/05/28 00:00 [pmc-release]
AID - 10.1007/s13246-023-01276-x [pii]
AID - 1276 [pii]
AID - 10.1007/s13246-023-01276-x [doi]
PST - ppublish
SO  - Phys Eng Sci Med. 2023 Sep;46(3):1071-1080. doi: 10.1007/s13246-023-01276-x. Epub 
      2023 May 28.