LONDON (Reuters), Oct 27 - A teenage cancer patient was mistakenly given massive overdoses of radiation for a brain tumor, mainly because of a mistake in paperwork, a report found on Friday.
Lisa Norris, 16, died at her home in Ayrshire, Scotland, last week, after battling the tumor for a year.
Speaking of the overdoses, the report said "the principal cause of this incident was identified as a single erroneous entry" on paperwork setting out her treatment plan at Beatson Oncology Centre in Glasgow.
Checks by senior staff also failed to spot the mistake.
The report, commissioned by the Scottish Executive, said "significant lessons" needed to be learned.
Norris suffered serious radiation burns as a result of the overdoses. She died after the brain tumor returned, but her father has said he is convinced it was the overdoses that were responsible. A postmortem has yet to report.
The inquiry report called for an immediate inspection of all Scotland's five specialist cancer centers.
"I hope today's report will help to ensure that the mistakes made in her treatment are not repeated," Scotland's Health Minister Andy Kerr said in a statement.
In January, Norris was given 19 overdoses of radiation therapy and shortly before she died she had undergone treatment to remove fluid from her brain.
Among criticisms in the report were that training records were out of date, written procedures failed to reflect current practice and inexperienced staff were used in the planning of the youngster's treatment.
Last Updated: 2006-10-27 10:37:42 -0400 (Reuters Health)
Related Reading
Girl given massive overdose of radiation dies, October 19, 2006
Copyright © 2006 Reuters Limited. All rights reserved. Republication or redistribution of Reuters content, including by framing or similar means, is expressly prohibited without the prior written consent of Reuters. Reuters shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon. Reuters and the Reuters sphere logo are registered trademarks and trademarks of the Reuters group of companies around the world.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
