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The effects of delays in radiotherapy treatment on tumour control.

There is often a considerable delay from initial tumour diagnosis to the start of radiotherapy treatment, which may be due to factors such as waiting lists and referral delays. This paper uses widely published models and clinical parameters to calculate the effect of delays in treatment on local tumour control for four different types of tumour-squamous cell carcinoma (head and neck), breast, cervix and prostate. The Poisson model for tumour control probability (TCP), an exponential function for tumour growth and the linear quadratic model of cell kill are used to calculate the change in TCP for delays between diagnosis and treatment of up to 100 days. Typical values of the clinical parameters have been taken from the literature; these include alpha and beta, sigma(alpha), tumour size at diagnosis, pre-treatment doubling time, delay in onset of accelerated repopulation and doubling time during treatment. It is acknowledged that there are limitations in the reliability of these data for predicting absolute values of tumour control, but models are still useful for predicting how changes in treatment parameters are likely to affect the outcome. It is shown that for fast-growing tumours a delay of 1-2 months can have a significant adverse effect on the outcome, whereas for slow-growing tumours such as Ca prostate a delay of a few months does not significantly reduce the probability of tumour control. These calculations show the importance of ensuring that delays from diagnosis through to treatment are minimized, especially for patients with rapidly proliferating tumours.

Appointments and Schedules↗

Modeling the effect of tamoxifen chemoprevention on long-term mortality in white women at high risk of breast cancer.

Tamoxifen has long been studied as a drug to treat breast cancer and now for its preventive effect. Intermediate results from studies on the preventive effect of tamoxifen vs. placebo endorsed tamoxifen's efficacy after a short follow-up. Effect of tamoxifen on long-term survival was simulated in women at a high risk of breast cancer. The model allows for tamoxifen effect on breast cancer according to estrogen receptor status, endometrial cancer, and pulmonary embolism. A virtual cohort of 200,000 white women was split into two treatment arms (placebo vs. tamoxifen), two age groups (35-49 and 50-74), and followed-up for 20 years. Incidence rates stemmed from the National Surgical Adjuvant Breast and bowel Project-P1, or the chemoprevention trials overview by Cuzick et al., and relative survival probabilities from surveillance, epidemiology, and end results. Six hypotheses of tamoxifen effects and their variations along time were considered. In the National Surgical Adjuvant Breast and Bowel Project-based results, women aged 35-49 took advantage of tamoxifen whatever the follow-up duration and hypothesis, except the one of only side effects remaining 15 years after treatment withdrawal. In the overview-based results, the advantage existed only when effect on pulmonary embolism stopped. Women aged 50-74 experienced underwent tamoxifen side effects that outweighed advantages whatever the follow-up duration and hypothesis, except the one of only beneficial remaining effect 15 years after treatment withdrawal. Overall, the slight positive effect of tamoxifen on long-term mortality should be cautiously interpreted for young women. In women aged over 50 years, however, tamoxifen chemoprevention could not be recommended.

Adult↗

Evaluation of a probabilistic model for staging of oesophageal carcinoma.

With the help of two experts in gastrointestinal oncology from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decision-support system is being developed for patient-specific therapy selection for oesophageal carcinoma. The kernel of the system is a probabilistic model describing the characteristics of oesophageal carcinoma and the pathophysiological processes of invasion and metastasis. Using data from 185 patients, an evaluation study of the model was conducted. We found that for 86% of the patients, the model established the stage of the patient's carcinoma correctly.

Artificial Intelligence↗