1.Comparison between IQQA liver image analysis system and manu-traced approaches on liver volume estimation in living donor liver transplantation
Lin WEI ; Wen-tao NG JIA ; Wei GAO ; Tao YANG ; Zhi-gui ZENG ; Hao WANG ; Zhong-yang SHEN ; Zhi-jun ZHU
Chinese Journal of Organ Transplantation 2012;33(6):351-353
Objective To investigate the safty and accuracy ot estimating the living donor's graft volume with IQQA liver imaging evaluation system.Methods Between June 2007 and July 2010,123living liver donors were enrolled to undergo 16-slice CT scanning,then graft volume was estimated by both IQQA and manu-traced multi-slice spiral computed tomography (MSCT) approach.The graft volume and time consuming between IQQA and manu-traced MSCT were compared. Pearson Correlation test was uesd to verify the correlation between the estimated graft volume estimated each method and actual graft weight detected in operation.Linear correlation analysis was done.Results The mean graft volume by IQQA and manu-traced MSCT was (856.76 ± 162.18) and (870.64 ±172.54) cm3 respectively preoperation.Paired t-test showed there was no statistically significant difference between IQQA and MSCT methods (P>0.05).It took mean ( 16.9 ± 1.4) min to calculatethe graft volume by IQQA and (39.3 ± 2.1 ) min by manu-traced MSCT,respectively (P<0.05).The real graft volume was (632.59 ± 13 1.73) cm3.Pearson correlation test showed the graft volume calculated by either IQQA or MSCT method had a significantly positive correlation with the real graft weight (MSCT r =0.921,IQQA r =0.896,P<0.01 ).Graft weight could be expressed in the equation:Actual graft weight =- 150.303 + 1.025 × IQQA value or =- 94.397 + 0.955 × MSCT value.Conclusion IQQA system has same accuracy with MSCT method in predicting the graft volume but consuming less time.IQQA system promotes the recognition of clinician on liver three dimensional anatomic structure.
2.EPOSTER • DRUG DISCOVERY AND DEVELOPMENT
Marwan Ibrahim ; Olivier D LaFlamme ; Turgay Akay ; Julia Barczuk ; Wioletta Rozpedek-Kaminska ; Grzegorz Galita ; Natalia Siwecka ; Ireneusz Majsterek ; Sharmni Vishnu K. ; Thin Thin Wi ; Saint Nway Aye ; Arun Kumar ; Grace Devadason ; Fatin Aqilah Binti Ishak ; Goh Jia Shen ; Dhaniya A/P Subramaniam ; Hiew Ke Wei ; Hong Yan Ren ; Sivalingam Nalliah ; Nikitha Lalindri Mareena Senaratne ; Chong Chun Wie ; Divya Gopinath ; Pang Yi Xuan ; Mohamed Ismath Fathima Fahumida ; Muhammad Imran Bin Al Nazir Hussain ; Nethmi Thathsarani Jayathilake ; Sujata Khobragade ; Htoo Htoo Kyaw Soe ; Soe Moe ; Mila Nu Nu Htay ; Rosamund Koo ; Tan Wai Yee ; Wong Zi Qin ; Lau Kai Yee ; Ali Haider Mohammed ; Ali Blebil ; Juman Dujaili ; Alicia Yu Tian Tan ; Cheryl Yan Yen Ng ; Ching Xin Ni ; Michelle Ng Yeen Tan ; Kokila A/P Thiagarajah ; Justin Jing Cherg Chong ; Yong Khai Pang ; Pei Wern Hue ; Raksaini Sivasubramaniam ; Fathimath Hadhima ; Jun Jean Ong ; Matthew Joseph Manavalan ; Reyna Rehan ; Tularama Naidu ; Hansi Amarasinghe ; Minosh Kumar ; Sdney Jia Eer Tew ; Yee Sin Chong ; Yi Ting Sim ; Qi Xuan Ng ; Wei Jin Wong ; Shaun Wen Huey Lee ; Ronald Fook Seng Lee ; Wei Ni Tay ; Yi Tan ; Wai Yew Yang ; Shu Hwa Ong ; Yee Siew Lim ; Siddique Abu Nowajish ; Zobaidul Amin ; Umajeyam Anbarasan ; Lim Kean Ghee ; John Pinto ; Quek Jia Hui ; Ching Xiu Wei ; Dominic Lim Tao Ran ; Philip George ; Chandramani Thuraisingham ; Tan Kok Joon ; Wong Zhi Hang ; Freya Tang Sin Wei ; Ho Ket Li ; Shu Shuen Yee ; Goon Month Lim ; Wen Tien Tan ; Sin Wei Tang
International e-Journal of Science, Medicine and Education 2022;16(Suppl1):21-37