Biological mechanisms of normal tissue damage: Importance for the design of NTCP models

Università degli Studi di Pavia, Italy
Radiotherapy and Oncology (Impact Factor: 4.36). 06/2012; 105(1). DOI: 10.1016/j.radonc.2012.05.008
Source: PubMed


The normal tissue complication probability (NTCP) models that are currently being proposed for estimation of risk of harm following radiotherapy are mainly based on simplified empirical models, consisting of dose distribution parameters, possibly combined with clinical or other treatment-related factors. These are fitted to data from retrospective or prospective clinical studies. Although these models sometimes provide useful guidance for clinical practice, their predictive power on individuals seems to be limited.

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    • "This does, however, not imply that there is any indication of a biological mechanism underlying the goodness-of-fit of the mathematical procedure. With regard to radiation effects in vivo, both in normal tissues and tumours, the potential of any radiobiology-based " model " over the simple mathematical (LQ) fit of the existing data – in face of our current knowledge – must be explicitly negated The clear conclusion from all these considerations is that the LQ model, and the corresponding alpha/beta-value (rather than " ratio " of alpha and beta!) is most useful for the calculation of equieffective doses [17], even though it is lacking any (radio)biological background [18]. Yet, it " works " in radiotherapy as long as doses per fraction are in the range of 0.5 and 6-8 Gy [17], even though it is apparently not related to any particular radiobiological mechanism. "
    Zeitschrift für Medizinische Physik 06/2015; 25:99-101. DOI:10.1016/j.zemedi.2014.12.009 · 2.96 Impact Factor
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    • "The pathways underlying the abnormalities occurring in the irradiated mesorectum reflect the effect of the radiolysis on the immunological system, explicating in particular in the activation of the leukocytes, most probably, through the release of the cytokines [13,18,22–24]. The accumulation of inflammatory cells, particularly evident in the perivascular recruitment of lymphocytes and eosinophils, and in the activation of fibroblasts in the connective tissue, has been demonstrated in patients treated with radiotherapy [22] [23] [24]. In our study, mainly in the cases who received radiotherapy plus chemotherapy, we observed accumulation of inflammatory cells around the vessel and the nerves composed by lymphocytes, plasma cells and eosinophils. "
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    ABSTRACT: Objective: In order to identify the radiotherapy-induced histological modifications in the mesorectum, we reviewed the surgical specimens of 90 rectal resections comprehensive of the total mesorectal excision (23 cases radiologically classified as cT2N0M0 and 67 as cT3N0M0). All patients were preoperative treated with radiotherapy: 20 with 50 Gy, 20 with 20 Gy and 50 Gy irradiation associated to FOLFOX scheme chemotherapy. Material and methods: Routine hematoxylin and eosin stained serial slides at 5 mm of intervals were obtained from surgical specimens and included the tumor site and the adjacent irradiated mucosa, the submucosa and the muscular layers of the rectal wall and the mesorectal adipose tissue, completely removed until to the mesorectal fascia. Ten subjects (eight cT2N0M0 and two cT3N0M0), who did not received preoperative oncological treatments were adopted as controls. Results: Histologically, examination revealed fibrosis of the adipose tissue in 86 cases (95%), vascular damage including vasculities and fibrotic thickening wall of arteries and veins in 46 cases (51%), sclero-hyalinosis of lymph nodes with pericapsular fibrosis in 22 cases (23%) and perineural deposition of fibrosis in 12 (13%). These findings were ubiquitously observed in the whole mesorectum. Fibrosis of the adipose tissue and vasculitis were mainly associated to the combination of 50 Gy radiations plus chemotherapy (p < 0.05). Conclusion: The detection of histopathological alterations in the mesorectum can give reason of the well-known postoperative complications and long-term sequels.
    Scandinavian Journal of Gastroenterology 12/2014; 50(2):1-7. DOI:10.3109/00365521.2014.983153 · 2.36 Impact Factor
    • "In addition, a consortium of leading radiation oncology experts has developed a model-based approach to select patients for proton therapy [37]. This methodology has been accepted by the Dutch health authorities despite the limited predictive performance of normal tissue complication probability (NTCP) models [38]. Taking into account the finite resources and the growing costs of health care, selecting subgroups of patients for specific treatment options, instead of delivering expensive treatments to the entire patient population, will be inevitable. "
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    ABSTRACT: Background Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models. Purpose In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors. Materials and methods Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis. Results A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models. Conclusion The models substantially outperformed ROs’ predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies.
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