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Dosimetric models

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Aditi Iyer edited this page Sep 16, 2023 · 16 revisions

Available models

FUNCTIONAL FORM EQUATION MODELS SITE
LKB Rectal bleeding[1],
late urinary toxicity[2],
late urinary toxicity post SBRT[3],
Radiation-induced liver disease[4] 		Prostate


Gastrointestinal
Linear Erectile dysfunction[5] Prostate
Logistic Esophagitis[6-8],
dysphagia[9], Pulmonary toxicity[18],
TCP[10] 		Lung
Biexponential Xerostomia[11] Head and neck
Cox $NTCP = 1 - e^{-H_0 e^{\beta_i X_i}}$ Cardiopulmonary toxicity[19] Lung
Others
Chest-wall pain Chest-wall pain[12] Lung
Fowler BED $BED = n.d(1 + d/(\alpha/\beta)) - (ln2(T -Tk))/(\alpha.T_p)$ BED(Lung)[20] Lung
TCP (Lung) TCP (Lung)[13] Lung
Appelt-correction Pneumonitis[14-16] Lung
TCP (Prostate) TCP (Prostate)[17] Prostate

Using dosimetric models in CERR

Model parameters are input as dictionaries (MATLAB structures), generated by parsing user-input JSON files. Dose and volume bins are computed from RT plans and structures available in the CERR archive.

  • Supported functional forms

    CERR_core/ModelImplementationLibrary/DosimetricModels

  • JSON Configuration files

    Parameters for various prediction models are defined via JSON files located at CERR_core/ModelImplementationLibrary/DosimetricModels/Models

  • Function signature

    Dosimetric models in CERR have the following signature:

       prediction = modelName(paramS, doseBinsV, volBinsV)
    • 'paramS' is a dictionary of model parameters of the form:
       paramS.parameterName.val = paramVal;

    Input parameters may be defined either by altering the JSON model files or via code using the above syntax.

    • Volumetric dose and structures defined in planC are used to compute dose and volume bins as follows:
      [dosesV,volsV] = getDVH(structNum,planNum,planC);
  [doseBinsV,volHistV] = doseHist(dosesV,volsV,binWidth);  %Note: Default DVHBinWidth is specified in CERROptions.json

    For models requiring DVH metrics from multiple structures, dose and volume bins should be passed as cell arrays.

      prediction = functionName(paramS, doseBinsC, volBinsC)

  • Examples

      global planC;
  indexS = planC{end};

  binWidth = 0.05;      % DVH bin width
  numFrx = 15;          % No. fractions delivered
  CERRpath = getCERRPath;

  %% Example 1 : Wijsman Esophagitis model

  %Read model parameters
  modelPath = fullfile(CERRpath,'ModelImplementationLibrary\DosimetricModels\Models\Esophagitis (Wijsman).json');
  modelS = jsondecode(fileread(modelPath));
  paramS = modelS.parameters;

  % Calc. DVH
  planNum = 1;
  structName = 'Esophagus'; %Replace with available structure name
  strListC = {planC{indexS.structures}.structureName};
  structNum = getMatchingIndex(structName,strListC,'EXACT'); %Structure index corresponding to 'Esophagus'
  [dosesV,volsV] = getDVH(structNum,planNum,planC);
  [doseBinsV,volHistV] = doseHist(dosesV,volsV,binWidth);
  correctedDoseC = frxCorrectROE(modelS,structNum,numFrx,{doseBinsV});
  correctedDoseV = correctedDoseC{1};
  ntcp =  feval(modelS.function,paramS,correctedDoseV,volHistV);


   %% Example 2 : Appelt Pneumonitis model

   %Read model parameters
   modelPath = fullfile(CERRpath,'ModelImplementationLibrary\DosimetricModels\Models\Pneumonitis (Appelt).json');
   modelS = jsondecode(fileread(modelPath));
   paramS = modelS.parameters;

   % E.g. inputting patient-specific clinical factors to a model
   % For a former smoker aged > 63 years:
   paramS.formerSmoker.val = 1;
   paramS.over63yrs.val = 1;

   % Calc. DVH
   structName = 'Lung-GTV'; %Replace with available structure name
   structNum = getMatchingIndex(structName,strListC,'EXACT'); %Structure index corresponding to 'Esophagus'
   [dosesV,volsV] = getDVH(structNum,planNum,planC);
   [doseBinsV,volHistV] = doseHist(dosesV,volsV,binWidth); 
   ntcp =  feval(modelS.function,paramS,doseBinsV,volHistV);

   %Example 3 : Sean-Walsh TCP model (prostate cancer)

   global planC;

   modelPath = fullfile(CERRpath,'ModelImplementationLibrary\DosimetricModels\Models\Prostate TCP (Sean Walsh).json');
   modelS = jsondecode(fileread(modelPath));
   paramS = modelS.parameters;

   structNum = 3; %Structure index corresponding to PTV
   paramS.numFractions.val = 5; %Define no. of fractions

   [dosesV,volsV] = getDVH(structNum,planNum,planC);
   [doseBinsV,volHistV] = doseHist(dosesV,volsV,binWidth); 
   tcp =  feval(modelS.function,paramS,doseBinsV,volHistV);

References

  1. Michalski, J. M., Gay, H., Jackson, A., Tucker, S. L., & Deasy, J. O. (2010). Radiation dose–volume effects in radiation-induced rectal injury. International Journal of Radiation Oncology* Biology* Physics, 76(3), S123-S129.

  2. Cheung, M. R., Tucker, S. L., Dong, L., De Crevoisier, R., Lee, A. K., Frank, S., ... & Kuban, D. (2007). Investigation of bladder dose and volume factors influencing late urinary toxicity after external beam radiotherapy for prostate cancer. International Journal of Radiation Oncology* Biology* Physics, 67(4), 1059-1065.

  3. Kole, T. P., Tong, M., Wu, B., Lei, S., Obayomi-Davies, O., Chen, L. N., ... & Collins, S. P. (2016). Late urinary toxicity modeling after stereotactic body radiotherapy (SBRT) in the definitive treatment of localized prostate cancer. Acta Oncologica, 55(1), 52-58.

  4. Pan, C. C., Kavanagh, B. D., Dawson, L. A., Li, X. A., Das, S. K., Miften, M., & Ten Haken, R. K. (2010). Radiation-associated liver injury. International Journal of Radiation Oncology* Biology* Physics, 76(3), S94-S100.

  5. Roach III, M., Chinn, D. M., Holland, J., & Clarke, M. (1996). A pilot survey of sexual function and quality of life following 3D conformal radiotherapy for clinically localized prostate cancer. International Journal of Radiation Oncology* Biology* Physics, 35(5), 869-874.

  6. Huang, E. X., Hope, A. J., Lindsay, P. E., Trovo, M., El Naqa, I., Deasy, J. O., & Bradley, J. D. (2011). Heart irradiation as a risk factor for radiation pneumonitis. Acta Oncologica, 50(1), 51-60.

  7. Wijsman, R., Dankers, F., Troost, E. G., Hoffmann, A. L., van der Heijden, E. H., de Geus-Oei, L. F., & Bussink, J. (2015). Multivariable normal-tissue complication modeling of acute esophageal toxicity in advanced stage non-small cell lung cancer patients treated with intensity-modulated (chemo-) radiotherapy. Radiotherapy and Oncology, 117(1), 49-54.

  8. Oh, J. H. (2018).

  9. Rancati, T., Schwarz, M., Allen, A. M., Feng, F., Popovtzer, A., Mittal, B., & Eisbruch, A. (2010). Radiation dose–volume effects in the larynx and pharynx. International Journal of Radiation Oncology* Biology* Physics, 76(3), S64-S69.

  10. Fontanella, A., Robinson, C., Zuniga, A., Apte, A., Thorstad, W., Bradley, J., & Deasy, J. (2014). SU‐E‐T‐312: Test of the Generalized Tumor Dose (gTD) Model with An Independent Lung Tumor Dataset. Medical Physics, 41(6Part16), 296-296.

  11. Chao, K. C., Deasy, J. O., Markman, J., Haynie, J., Perez, C. A., Purdy, J. A., & Low, D. A. (2001). A prospective study of salivary function sparing in patients with head-and-neck cancers receiving intensity-modulated or three-dimensional radiation therapy: initial results. International Journal of Radiation Oncology* Biology* Physics, 49(4), 907-916.

  12. Din, S. U., Williams, E. L., Jackson, A., Rosenzweig, K. E., Wu, A. J., Foster, A., ... & Rimner, A. (2015). Impact of fractionation and dose in a multivariate model for radiation-induced chest wall pain. International Journal of Radiation Oncology* Biology* Physics, 93(2), 418-424.

  13. Jeong, J., Oh, J. H., Sonke, J. J., Belderbos, J., Bradley, J. D., Fontanella, A. N., ... & Deasy, J. O. (2017). Modeling the cellular response of lung cancer to radiation therapy for a broad range of fractionation schedules. Clinical Cancer Research, 23(18), 5469-5479.

  14. Appelt, A. L., Vogelius, I. R., Farr, K. P., Khalil, A. A., & Bentzen, S. M. (2014). Towards individualized dose constraints: adjusting the QUANTEC radiation pneumonitis model for clinical risk factors. Acta Oncologica, 53(5), 605-612.

  15. Thor, M., Deasy, J., Iyer, A., Bendau, E., Fontanella, A., Apte, A., ... & Jackson, A. (2019). Toward personalized dose-prescription in locally advanced non-small cell lung cancer: Validation of published normal tissue complication probability models. Radiotherapy and Oncology, 138, 45-51.

  16. Oh, J. H. (2018).

  17. Walsh, S., Roelofs, E., Kuess, P., Lambin, P., Jones, B., Georg, D., & Verhaegen, F. (2016). A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy. Medical physics, 43(2), 734-747.

  18. Tekatli H., Duijm M., Oomen-de Hoop E., Verbakel W., Schillemans W., Slotman B.J., Nuyttens J.J., Senan S. (2018) Normal Tissue Complication Probability Modeling of Pulmonary Toxicity After Stereotactic and Hypofractionated Radiation Therapy for Central Lung Tumors. Int J Radiat Oncol Biol Phys.100(3):738-747. doi: 10.1016/j.ijrobp.2017.11.022. Epub 2017 Nov 21. PMID: 29413285.

  19. Thor, M., Deasy, J.O., Hu, C., Gore, E., Bar-Ad, V., Robinson, C., Wheatley, M., Oh, J.H., Bogart, J., Garces, Y.I. and Kavadi, V.S. (2020). Modeling the impact of cardiopulmonary irradiation on overall survival in NRG oncology trial RTOG 0617. Clinical Cancer Research. 26(17), pp.4643-4650

  20. Fowler, J.F.(2001). Biological factors influencing optimum fractionation in radiation therapy. Acta oncologica, 40(6), pp.712-717.

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