LASSO NTCP predictors for the incidence
of xerostomia in patients with head and
neck squamous cell carcinoma and
nasopharyngeal carcinoma
Tsair-Fwu Lee
1, Ming-Hsiang Liou
2,3, Yu-Jie Huang
4, Pei-Ju Chao
1,4, Hui-Min Ting
1,4, Hsiao-Yi Lee
2& Fu-Min Fang
41Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences,
Kaohsiung, Taiwan, ROC,2Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung,
Taiwan, ROC,3Department of Radiation Oncology, Kaohsiung Yuan’s General Hospital, Kaohsiung, Taiwan, ROC,4Department of
Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan, ROC.
To predict the incidence of moderate-to-severe patient-reported xerostomia among head and neck
squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC) patients treated with
intensity-modulated radiotherapy (IMRT). Multivariable normal tissue complication probability (NTCP)
models were developed by using quality of life questionnaire datasets from 152 patients with HNSCC and 84
patients with NPC. The primary endpoint was defined as moderate-to-severe xerostomia after IMRT. The
numbers of predictive factors for a multivariable logistic regression model were determined using the least
absolute shrinkage and selection operator (LASSO) with bootstrapping technique. Four predictive models
were achieved by LASSO with the smallest number of factors while preserving predictive value with higher
AUC performance. For all models, the dosimetric factors for the mean dose given to the contralateral and
ipsilateral parotid gland were selected as the most significant predictors. Followed by the different clinical
and socio-economic factors being selected, namely age, financial status, T stage, and education for different
models were chosen. The predicted incidence of xerostomia for HNSCC and NPC patients can be improved
by using multivariable logistic regression models with LASSO technique. The predictive model developed in
HNSCC cannot be generalized to NPC cohort treated with IMRT without validation and vice versa.
R
ecently, Beetz et al.
1reported that the normal tissue complication probability (NTCP) models developed in
a population treated with a specific technique could not be generalized and extrapolated to a population
treated with another technique without external validation. They showed that 3D conformal radiotherapy
(3D-CRT)-based models for patient-reported xerostomia among head and neck cancers (HNC) patients treated
with primary radiotherapy (RT) turned out to be less valid for patients treated with intensity-modulated
radio-therapy (IMRT), so the 3D-CRT NTCP models cannot be used for IMRT cohorts.
In addition, we performed a validation test of the Quantitative Analyses of Normal Tissue Effects in the Clinic
(QUANTEC) guidelines against quality of life (QoL) questionnaire datasets collected prospectively from patients
with HNC, including head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC)
2.
We have found that the QoL datasets validate the QUANTEC guidelines and suggest that the modified
QUANTEC 20/20-Gy spared-gland guideline is suitable for clinical use in HNSCC cohorts to effectively avoid
xerostomia, and that the QUANTEC 25-Gy guideline is justified for NPC cohorts, implying that a difference
exists between the two cohorts that needs to be investigated. NPC is a specific entity different from head and neck
carcinoma
3.
HNSCC develops from the mucosal linings of the upper aerodigestive tract, comprising 1) the nasal cavity and
paranasal sinuses, 2) the oropharynx, 3) the hypopharynx, larynx, and 4) the oral cavity. NPC is a carcinoma
arising in the nasopharynx that shows light microscopic or ultrastructural evidence of squamous differentiation.
It encompasses squamous cell carcinoma, non-keratinizing carcinoma (differentiated or undifferentiated), and
basaloid squamous cell carcinoma
4,5. The disease behavior of NPC is different from HNSCC. The treatment
OPEN
SUBJECT AREAS:
OUTCOMES RESEARCH RADIOTHERAPYReceived
13 May 2014
Accepted
8 August 2014
Published
28 August 2014
Correspondence and requests for materials should be addressed to T.-F.L. ([email protected]. tw)strategies are also different. Approximately 90% of NPC patients
develop lymphadenopathy and 50% of patients have bilateral lymph
node involvement. Because the nasopharynx is immediately adjacent
to the base of the skull, surgical resection with an acceptable margin is
impossible. Radiation therapy is the sore treatment of NPC
6,7.
However, surgical resection with a safe margin is the treatment of
choice for HNSCC. Therefore, the doses and fields of radiation
ther-apy are different from NPC and HNSCC. With the advances of
adjuvant treatment, concurrent chemotherapy may be considered
according to patient’s disease status to improve the control rate both
in HNSCC and NPC. Whatever, the disease itself should not affect
salivary flow or the patient’s perception of salivary flow independent
of radiation dose to those salivary glands. To ensure that xerostomia
was induced primarily by the radiation treatment, patients with
moderate-to-severe xerostomia at baseline need to be excluded from
the analysis
8–11.
Developing a multivariable logistic regression model requires an
answer to the question of the number of predictive factors to include.
Some predictive factors such as clinical and treatment-related factors
that may have important effects on the risk of radiation-induced
complications need to be taken into consideration. Xu et al.
12–14introduced least absolute shrinkage and selection operator
(LASSO) to build NTCP models of xerostomia after 3D-CRT for
HNC. De Ruyck et al.
15developed a multicomponent prediction
model for acute esophagitis in lung cancer patients using LASSO.
Our previous study developed a multivariate logistic regression
model with LASSO to make valid predictions about the incidence
of patient-reported xerostomia for HNC patients
10. These reports all
recommended the LASSO method for multivariable logistic
regres-sion NTCP modeling
12,15.
The goals of this study were to characterize the incidence of
moderate-to-severe patient-reported xerostomia among HNSCC
and NPC patients treated with curative-intent IMRT and to find
clinical and dosimetric factors associated with the toxicity.
Specifically, we sought to explore the use of LASSO that
incorpo-rates the bootstrapping technique to develop multivariable logistic
regression models that can be used to predict the incidence of
moderate-to-severe patient-reported xerostomia for HNSCC and
NPC patients. On the basis of the associations identified, it would
then be possible to offer an efficient set of predictive factors to
limit the risk of xerostomia for HNSCC and NPC patients treated
with IMRT.
Figure 1
|
The scatter plots of the mean dose (a, b) and the differences in dose distributions to the contralateral and the ipsilateral parotid glands between the HNSCC and NPC cohorts (c, d). Abbreviation: HNSCC: head and neck squamous cell carcinoma; NPC: nasopharyngeal carcinoma.www.nature.com/scientificreports
Gung medical foundation institutional review board (99-1420B, 96-1231B) and all participants gave written informed consent; and all experiments were performed in accordance with relevant guidelines and regulations.
IMRT techniques.All patients were treated with IMRT as described in detail in previous publications2. For the IMRT planning goal, the mean dose to each parotid
gland should be kept as low as possible, consistent with the desired clinical target volume coverage. The IMRT technique reduces the mean parotid dose, reducing xerostomia, as assessed by the Radiation Therapy Oncology Group (RTOG) xerostomia-related questionnaire score28. Sparing at least one parotid gland appears
to eliminate complications25. Dose distributions were calculated and dose-volume
histograms (DVHs) were generated separately for each parotid gland, enabling separate analysis. Two IMRT techniques were used: simultaneous integrated boost (SIB) and sequential mode (SQM). The prescribed total dose ranged from 54.0 to 77.4 Gy (median, 70.0 Gy). Details about the prescribed dose and fractions for the SIB and SQM techniques can be found in previous studies29,30.
Chemotherapy.Ninety-four HNSCC patients and seventy-five NPC patients received concurrent chemotherapy for XER3m. The regimens used involved with weekly CDDP regimen, PF regimen (cisplatin 1 fluorouracil) for 2–6 courses, or modified regimens according to patient’s disease status by medical oncologist. QoL evaluation.A prospective survey of QoL using the European Organization for Research and Treatment of Cancer (EORTC) C30 and H&N35 QoL questionnaires (QLQ-C30 and QLQ-H&N35) was performed on 152 patients with HNSCC and 84 patients with NPC. Details about the QoL evaluation can be found in previous studies2,10. The patients were asked to complete the questionnaire prior to treatment
and 3 months, 6 months, 1 year, and 2 years after IMRT. For the purposes of this analysis, the 3-month and 12-month follow-up time points were used. Chinese versions of the EORTC QLQ-C30 and QLQ-H&N35 questionnaires were obtained from the Quality of Life Unit, EORTC Data Center, Brussels, Belgium2,31. For each
item on the EORTC QLQ-C30 and QLQ-H&N35 questionnaires, the following four-point Likert scale was used: none (0), a little (33), quite a lot (66), and a lot (100). All QoL scores are given in the text. A high score on the functional or global QoL scale represents a relatively high/healthy level of functioning or global QoL, whereas a high score on the symptom scale represents the presence of a symptom or problem. The EORTC QLQ-H&N35 questionnaire was used to evaluate the analytical endpoint for xerostomia, and only the dry month item was used for this study. The primary endpoint was defined as moderate (66) to severe (100) xerostomia at 3 (XER3m) and
12 months (XER12m) after the completion of IMRT; this corresponds to the two
highest scores on the four-point Likert scale. As we were primarily interested in severe xerostomia induced by RT itself, patients with moderate–to-severe xerostomia at baseline were excluded from further analysis1,8,10,16,22.
Statistical analysis.We aimed to develop a multivariable logistic regression NTCP model with LASSO to make valid predictions about the risk of moderate-to-severe patient-reported xerostomia using QoL datasets. The multivariable logistic regression analysis, with an extended bootstrapping technique, was used as described by El Naqa et al.17and Beetz et al.1,8,16.
For each patient, predictive values were calculated for each set of predictive factors based on the multivariable logistic regression coefficients according to the following formula: NTCP~(1ze{S){1, where S~b 0z Xn i~1 bi:xi ð1Þ
in which n is the number of predictive factors in the built model; variables xirepresent
different predictive factors; and biare the corresponding regression coefficients.
For each HNSCC patient, 17 candidate predictive factors were initially included in the variable selection procedure. The candidates included 15 clinical and two dosi-metric factors. For each NPC patient, 15 candidate predictive factors were initially included in the variable selection procedure. The candidates included 13 clinical and two dosimetric factors. The dosimetric candidate factors were the mean dose given to the contralateral parotid gland c) and the ipsilateral parotid gland (Dmean-i) (Gy). We excluded Vx values, which were previously found to be highly correlated with each other10,16; Dmean-c and Dmean-i were the only two DVH-parameters in
this study. We used the LASSO process to select the optimal numbers of potential predictive factors for the NTCP predictive model. The LASSO was first proposed by Tibshirani in 199632; the details can be found in previous studies10,12,13. It uses the
following equation to shrink the coefficients and select the predictive factors: arg min b kY{Xbk 2subject to bk k~Xd j~0 bj ƒt ð2Þ where d is the number of variables selected, and t is tuning parameters that control the degree of penalty, which can be determined by cross-validation. Details can be found in previous studies10,12,33. However, in order to generalize the use of the models, a
compact model can be generated by manually setting the value of t (to set like a penalty). In this study, the goal was achieved when the optimal selected number of predictive factors was set to no more than three if the AUC $ 0.85. After selecting the predictive factors, the system performance can be checked by using
the AUC, scaled Brier score, Nagelkerke R2, Omnibus, and Hosmer-Lemeshow
test1,2,8,16.
External validations were checked to answer the question arisen as to whether predictive model developed among HNSCC patients are also valid among those patients with NPC who treated with IMRT and vice versa. System performance was checked by the same methods used above.
Single contralateral parotid gland and the ipsilateral parotid gland mean dose model conserved traditional techniques were considered for convenience use. The parameters for the univariate NTCP regression model are shown. Statistical analyses were performed using SPSS 19.0 (SPSS, Chicago, IL, USA).
1. Beetz, I. et al. External validation of three dimensional conformal radiotherapy based NTCP models for patient-rated xerostomia and sticky saliva among patients treated with intensity modulated radiotherapy. Radiother Oncol 105, 94–100 (2012).
2. Lee, T.-F. & Fang, F.-M. Quantitative analysis of normal tissue effects in the clinic (QUANTEC) guideline validation using quality of life questionnaire datasets for parotid gland constraints to avoid causing xerostomia during head-and-neck radiotherapy. Radiother Oncol 106, 352–358 (2013).
3. Bensouda, Y. et al. Treatment for metastatic nasopharyngeal carcinoma. Eur Ann Otorhinolaryngol Head Neck Dis 128, 79–85 (2011).
4. Barnes, L., Organization, W. H. & Cancer, I. A. f. R. o. Pathology and genetics of head and neck tumours. (World Health Organization, 2005).
5. Bonner, J. A. et al. Radiotherapy plus cetuximab for squamous-cell carcinoma of the head and neck. N Engl J Med 354, 567-578, doi:10.1056/NEJMoa053422 (2006).
6. Perez, C. A. et al. Carcinoma of the nasopharynx: factors affecting prognosis. Int J Radiat Oncol Biol Phys 23, 271–280 (1992).
7. Halperin, E. C., Brady, L. W., Wazer, D. E. & Perez, C. A. Perez & Brady’s Principles and Practice of Radiation Oncology. (Lippincott Williams & Wilkins, 2013).
8. Beetz, I. et al. NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: The role of dosimetric and clinical factors. Radiother Oncol 105, 101–106 (2012). 9. Beetz, I. et al. The QUANTEC criteria for parotid gland dose and their efficacy to
prevent moderate to severe patient-rated xerostomia. Acta Oncologica, 1–8 (2013).
10. Lee, T.-F. et al. Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer. PLoS ONE 9, 1–11; e89700, doi:10.1371/journal.pone.0089700 (2014). 11. Lee, T.-F. et al. Normal tissue complication probability model parameter
estimation for xerostomia in head and neck cancer patients based on scintigraphy and quality of life assessments. BMC cancer 12, 567 (2012).
12. Xu, C.-J., van der Schaaf, A., Schilstra, C., Langendijk, J. A. & van’t Veld, A. A. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models. Int J Radiat Oncol Biol Phys 82, e677–e684 (2012).
13. Xu, C.-J., van der Schaaf, A., Van’t Veld, A. A., Langendijk, J. A. & Schilstra, C. Statistical validation of normal tissue complication probability models. Int J Radiat Oncol Biol Phys 84, e123–e129 (2012).
14. Xu, J. & Yin, J. Kernel least absolute shrinkage and selection operator regression classifier for pattern classification. Comput Vis, IET 7, 48–55 (2013).
15. De Ruyck, K. et al. Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemoradiotherapy. Int J Radiat Oncol Biol Phys 81, 537–544 (2011).
16. Beetz, I. et al. Development of NTCP models for head and neck cancer patients treated with three-dimensional conformal radiotherapy for xerostomia and sticky saliva: The role of dosimetric and clinical factors. Radiother Oncol 105, 86–93 (2012).
17. El Naqa, I. et al. Multivariable modeling of radiotherapy outcomes, including dose–volume and clinical factors. Int J Radiat Oncol Biol Phys 64, 1275–1286 (2006).
18. van de Water, T. A., Bijl, H. P., Westerlaan, H. E. & Langendijk, J. A. Delineation guidelines for organs at risk involved in radiation-induced salivary dysfunction and xerostomia. Radiother Oncol 93, 545–552 (2009).
19. Lyman, J. T. Complication probability as assessed from dose-volume histograms. Radiat Res 104, S13–S19 (1985).
20. Skwarchuk, M. W. et al. Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose–response. Int J Radiat Oncol Biol Phys 47, 103–113 (2000).
21. Ramsey, S. D. et al. Quality of life in survivors of colorectal carcinoma. Cancer 88, 1294–1303 (2000).
22. Fang, F.-M. et al. Multivariate analysis of quality of life outcome for
nasopharyngeal carcinoma patients after treatment. Radiother Oncol 97, 263–269 (2010).
23. Miah, A. B. et al. Dose–response analysis of parotid gland function: What is the best measure of xerostomia? Radiother Oncol 106, 341–345 (2013).
24. Kam, M. K. et al. Prospective randomized study of intensity-modulated radiotherapy on salivary gland function in early-stage nasopharyngeal carcinoma patients. J Clini Oncol 25, 4873–4879 (2007).
www.nature.com/scientificreports
25. Moiseenko, V. et al. Treatment planning constraints to avoid xerostomia in head-and-neck radiotherapy: an independent test of QUANTEC criteria using a prospectively collected dataset. Int J Radiat Oncol Biol Phys 82, 1108–1114 (2012). 26. Deasy, J. O. et al. Radiotherapy dose–volume effects on salivary gland function. Int
J Radiat Oncol Biol Phys 76, S58–S63 (2010).
27. Kim, D. R. et al. A predictive model for dysphagia following IMRT for head and neck cancer: Introduction of the EMLasso technique. Radiother Oncol 107, 295–299 (2013).
28. Marucci, L. et al. Influence of intensity-modulated radiation therapy technique on xerostomia and related quality of life in patients treated with intensity-modulated radiation therapy for nasopharyngeal cancer. Head Neck-J Sci Spec 34, 328–335, doi:10.1002/hed.21736 (2012).
29. Lee, T.-F., Ting, H.-M., Chao, P.-J. & Fang, F.-M. Dual Arc volumetric-modulated Arc radiotherapy (VMAT) of nasopharyngeal carcinomas: a simultaneous integrated boost treatment plan comparison with intensity-modulated radiotherapies and single arc VMAT. Clini Oncol 24, 196–207 (2012). 30. Leung, S. W. & Lee, T.-F. Treatment of nasopharyngeal carcinoma by
tomotherapy: five-year experience. BMC Cancer 5, 9, 1–9 (2013).
31. Chen, W.-C. et al. Scintigraphic assessment of salivary function after intensity-modulated radiotherapy for head and neck cancer: Correlations with parotid dose and quality of life. Oral Oncol 49, 42–48 (2013).
32. Tibshirani, R. Regression shrinkage and selection via the lasso. J Roy Stat Soc B 267–288 (1996).
33. Hastie, T., Tibshirani, R., Friedman, J. & Franklin, J. The elements of statistical learning: data mining, inference and prediction. The Math Intellg 27, 83–85 (2005).
Acknowledgments
We thank Chun-Ming Chang and Jing-Chuan Jiang for statistical technical supports. This study was supported financially, in part, by grants from NSC-101-2221-E-151-007-MY3 and NSC-102-2221-E-182A-002. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author contributions
T.F.L.: original idea, study design, analysis and interpretation of data, statistical analyses, and writing of manuscript; M.H.L., P.J.C., Y.J.H. and H.Y.L.: analysis and interpretation of data, statistical analysis; H.M.T. and H.Y.L.: technical, material support, and statistical analyses; F.M.F.: data collection and technical supports; All authors read and approved the final manuscript.
Additional information
Supplementary informationaccompanies this paper at http://www.nature.com/ scientificreports
Competing financial interests:The authors declare no competing financial interests. How to cite this article:Lee, T.-F. et al. LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma. Sci. Rep. 4, 6217; DOI:10.1038/srep06217 (2014).
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http:// creativecommons.org/licenses/by-nc-sa/4.0/
