Our previous studies have indicated that the optimal dosage ratio of pretargeting antibody to effector is proportional to their maximum percent tumour accumulations (MPTAs). This study quantitatively describes how both MPTAs and their ratio change with tumour size, to simplify pretargeting optimisation when tumour size varies. The CC49 antibody dosages below saturation of the tumour antigen level were first examined for the LS174T tumour mouse model. Then the MPTAs of the antibody in mice bearing tumours of different sizes were determined, always at antibody dosages below antigen saturation. Historical data from this laboratory were used to collect the MPTAs of the (99m)Tc-cMORF effector for different tumour sizes, always at effector dosages below that required to saturate the MORF in tumour. The MPTAs versus tumour sizes for both the antibody and the effector were fitted non-linearly. The best fit of the antibody MPTA (Y(antibody)) with tumour size (x) in grams was Y(antibody)=19.00 x(-0.65) while that for the effector was Y(effector)=4.51x(-0.66). Thus, even though the MPTAs of both vary with tumour size, the ratio (Y(antibody)/Y(effector)) is a constant at 4.21. In conclusion, the MPTA ratio of the antibody to the effector was found to be constant with tumour size, an observation that will simplify pretargeting optimisation because remeasurement of the optimum dosage ratio for different tumour sizes can be avoided. Theoretical considerations also suggest that this relationship may be universal for alternative antibody/effector pairs and for different target models, but this must be experimentally confirmed.
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http://dx.doi.org/10.1016/j.ejca.2009.09.007 | DOI Listing |
World J Surg Oncol
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Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao University, NO.1677 Wutaishan Road, Qingdao, Shandong Province, 266555, China.
Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.
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BMC Cancer
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Department of Interventional Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China.
Background: The appropriateness of ablation for liver cancer patients meeting the Milan criteria remains controversial.
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BMC Med Res Methodol
January 2025
Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.
Background: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China.
This study aims to develop an end-to-end deep learning (DL) model to predict neoadjuvant chemotherapy (NACT) response in osteosarcoma (OS) patients using routine magnetic resonance imaging (MRI). We retrospectively analyzed data from 112 patients with histologically confirmed OS who underwent NACT prior to surgery. Multi-sequence MRI data (including T2-weighted and contrast-enhanced T1-weighted images) and physician annotations were utilized to construct an end-to-end DL model.
View Article and Find Full Text PDFSci Rep
January 2025
Radiation Safety and Quality Assurance division, National Cancer Center Hospital East, Chiba, 277-8577, Japan.
The impact of three-dimensional (3D) dose delivery accuracy of C-arm linacs on the planning target volume (PTV) margin was evaluated for non-coplanar intracranial stereotactic radiosurgery (SRS). A multi-institutional 3D starshot test using beams from seven directions was conducted at 22 clinics using Varian and Elekta linacs with X-ray CT-based polymer gel dosimeters. Variability in dose delivery accuracy was observed, with the distance between the imaging isocenter and each beam exceeding 1 mm at one institution for Varian and nine institutions for Elekta.
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