Analysis of survival for lung cancer resections cases with fuzzy and soft set theory in surgical decision making.

OBJECTIVE: Lung cancer is the most common type of cancer around the world, and it represents the main cause of death in the USA. Surgical treatment is the optimal therapeutic strategy for resectable non-small cell lung cancer. The principal factor for long-term survival after complete resection is the anatomic extension of the neoplasm. However, other factors also have adverse effects on operative mortality, and influence long-term outcome. In this paper we propose an algorithmic solution for the estimation of 5-years survival rate in lung cancer patients undertaking pulmonary resection.
MATERIALS AND METHODS: We address the issue of survival analysis through decision-making techniques based on fuzzy and soft set theories. We develop an expert system based on clinical and functional data of lung cancer resections in patients with cancer that can be used to predict the survival of patients.
RESULTS: The evaluation of surgical risk in patients undertaking pulmonary resection is a primary target for thoracic surgeons. Lung cancer survival is influenced by many factors. The computational performance of our algorithm is critically analyzed by an experimental study. The correct survival classification is achieved with an accuracy of 79.0%. Our novel soft-set based criterion is an effective and precise diagnosis application for the determination of the survival rate.

Introduction

A great difficulty in designing intelligent systems consists in the absence of a knowledge base that collects all the evidence and common sense [1]. The researcher must resort to mathematical principles that catch imprecision or uncertainty in the available data. Through partial membership, fuzzy set theory provides a successful approach to this problem. The partial membership property permits a gradual compliance and allows us to define sets of a non-classical form. The use of fuzzy sets has been widely extended to important theoretical and applied fields since the publication of Zadeh’s seminal paper [2].

On the other hand, the theory of soft sets started with the research of Molodtsov [3] who introduced the theoretical foundations and also showed its application to several areas.

Molodtsov proved that his soft set model incorporates the fuzzy sets model. Alcantud [4] establishes other relationships among extended soft set and fuzzy set models. Other important references in this area are: Maji, Biswas and Roy [5], Aktaş & Çağman [6], and Maji et al. [7]. Decision-making (DM) procedures under incomplete information were investigated by Zou and Xiao [8], Han et al. [9], and Qin et al. [10]. Alcantud and Santos-García [11, 12] have recently made some theoretical contributions to decision making with incomplete information under a renewed Laplacian perspective. Fatimah et al. [13] introduce a probabilistic approach to soft sets. Zhan et al. [14-16] show an interesting survey and reviews on decision making methods based on soft sets and rough soft sets. Peng et al. [17, 18] develop some decision making methods for interval-valued fuzzy soft sets. Some recent articles in other fields are [19, 20].

Beyond theoretical developments and investigations, the development of real-life applications that handle the vague, imprecise, inconsistent and uncertain knowledge have proliferated in numerous fields in recent decades [21]. Soft computing is an emerging approach to computing, which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision [22].

The major areas of application of soft computing are: robotics and machine control (path planning, control, coordination, and decision making), natural language processing (representation and understanding), speech and character recognition (understanding, image processing, and biometrics [23]), biomedical systems and bioinformatics (Santos-Buitrago et al. [24] define a real-life application for decision making under incomplete information in the field of symbolic computational biology [25]), and big data and data mining (extract rules, features, analysis, and trends from large databases, e.g. social networks or financial series).

Soft computing techniques offer good solutions to optimization and decision making (e.g. [26]). The applications to assist physicians and surgeons and help them make decisions regarding the presence or absence of diseases are discussed in more detail at the end of this Introduction.

In this paper we design a procedure to predict patient survival in lung cancer resections from a soft sets and fuzzy sets theory perspective. Our thoracic surgery application continues the research conducted by Varela et al. [27]. We develop an expert system based on clinical and functional data of lung cancer resections in patients with cancer which can be used to predict the survival of patients.

Survival analysis in lung cancer resections

In worldwide terms lung cancer is the most frequent type of cancer, and it represents the main cause of death in males as in females in the USA [28]. About 27% of cancer deaths correspond to lung cancer. The 5-year survival rate for lung cancer is 55% for cases detected when the cancer is still localized. The diagnosis of lung cancer at an early stage is very low (about 16%); besides, over 50% of lung cancer patients die before the first year after being diagnosed [29, 30]. Smoking is one of the most important causes of small and non-small cell lung cancer, which represent a percentage greater than 80% of lung cancer deaths for women and an even higher percentage (90%) for men [31].

The type of pulmonary resections with a higher morbidity and mortality rate is the pneumonectomy [32]. Factors affecting the complication and mortality after pneumonectomy for malignant disease are diverse [33, 34]. Bernard et al. [35] establish some factors adversely affecting postoperative complications: preoperative chemotherapy, comorbidity indices, type of lung resection, extended resection, forced expiratory volume in 1 second, bronchoplastic technique, and pulmonary pathology. According to Licker et al. [36], major non-fatal complications are heart failure, respiratory failure, pulmonary embolism, myocardial infarct, and stroke.

The risk factors, postoperative morbidity, and mortality rate were analyzed by Duque et al. [37] in patients who underwent thoracotomy for bronchogenic carcinoma. Postoperative mortality rate was significantly higher in patients which have undergone pneumonectomy surgery and in those with vascular disease. In general surgery, the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity scoring system (POSSUM) constitutes an instrument for evaluating the surgical outcome [38].

Kearney et al. [39] proved that small values on predicted postoperative FEV1 are an optimal indicator of a high risk with regard to the morbidity in patients undergoing lung resection. Age also increases the surgical risk. Elderly patients usually have different kinds of comorbidities and have a below-average overall performance. The 5-year survival rate of convalescents under 65 was significantly better compared to patients 65–75 years old [40].

When surgical resection is not possible, survival rate is low for all treatments after diagnosis. More than 25% of patients who underwent surgery succeed in surviving for 5 years [41]. A high proportion of patients, 30% to 60%, can have a broad range of post-operative cardiopulmonary complications [37]. The most appropriate choice for resectable non-small cell lung cancer is the surgical treatment. The main factor for long-term survival after complete resection is the anatomic extension of the neoplasm. However, operative mortality is negatively affected by other factors (e.g., presence of the chronic obstructive pulmonary disease or advanced age [42-44]), which influences long-term outcome. In a previous investigation [27], we proposed several variables to predict the occurrence of post-operative cardio-respiratory complications, some of them related to a long-term outcome: tumor staging of the patient, extent of resection, chest wall resection, FEV1% (forced expiratory volume in 1 second percent), ppoFEV1% (predicted post-operative forced expiratory volume in 1 second percent), induction chemotherapy, ischemic heart disease, cardiac arrhythmia, diabetes mellitus, perioperative blood transfusion, sex, age, and body mass index.

The risk of cancer recurrence after treatment decreases in the following years, but cancer can recur five years later. The five years survival rate, used in oncology, merely indicates a cut-off point used to facilitate the comparison of the performance of different types of possible treatments and to indicate the prognosis of a particular cancer at a particular stage.

Soft computing techniques in diagnosis

Previous developments of soft computing techniques in diagnosis have inspired our approach. Yuksel et al. [45] used soft set theory for the diagnosis of prostate cancer risk. Alcantud et al. [46] provides an implementation framework for glaucoma diagnosis with the use of soft sets. Deli and Çağman [47] define several similarity measures based on intuitionistic fuzzy soft sets and give some applications for diagnosis of diseases. Tozlu et al. [48] perform a comparative study for medical diagnosis of prostate cancer using a multi-criteria decision making method, which includes earlier analyses like [45]. Sreedevi et al. [49] propose an interesting application to classify malignant/normal micro-calcifications on mammograms. Slowiński provides an interesting contribution of rough sets to the investigation of information systems made up of duodenal ulcer patients treated by highly selective vagotomy [50]. Son and Thong [51] integrate intuitionistic fuzzy sets and recommender systems and put forward an intuitionistic fuzzy recommender system that provides support for medical diagnosis. This includes an intuitionistic fuzzy collaborative filtering method (IFCF) that forecasts possible disorders. Moreover, Thong and Son [52] put forward hybrid intuitionistic fuzzy collaborative filtering to improve IFCF. Their novel proposal derives from hybridization between intuitionistic fuzzy recommender systems and picture fuzzy clustering. Li et al. [53] merge Dempster-Shafer’s theory of evidence and grey relational analysis to support medical diagnosis by taking advantage of decision making methodologies for fuzzy soft sets. Finally, Wang et al. [54] improve the performance of [53] by diminishing the associated uncertainty and increasing the choice decision level. Also noteworthy are the diagnosis approaches of Deli with other co-authors on soft-computing. They employ different combinations of neutrosophic sets and soft sets [55-58] for applications in medical diagnosis.

Our proposal is close to the approach used by Yuksel et al. [45], although our method is different. Divergences are evident on issues such as validation and the use of our database to evaluate the predictive power of the method. Our methodology bypasses technically disputable stages in Yuksel’s approach.

In our method, the raw data are initially fuzzified. The soft rules are then generated from associated soft sets. These soft rules facilitate our prediction system by means of which the survival can be assessed.

The data used in our model correspond to the following characteristics of the patients: age of patient, body mass index, chronic obstructive pulmonary disease, forced vital capacity calculated percentage (see Fig 1), approach of the surgery, and complications in the surgery. Fig 1 shows the typical behavior of the forced vital capacity calculated percentage (FEV1%) and the forced vital capacity (FVC) for patients in normal situations and in obstructive cases. Two types of approaches, video assisted thoracoscopy (VATS) and open thoracotomy, of the surgery are shown in Fig 2.

Fig 1

FEV1% and FVC graphs.

FEV1% and FVC graphs in normal/obstructive cases. Source: own elaboration.

Fig 2

Approach of the surgery.

Diagram showing video assisted thoracoscopy (left) and open thoracotomy (right). Source: own elaboration.

This paper is organized as follows. Section Fuzzy sets and soft sets: definitions recalls some definitions and notions on fuzzy set and soft set. In Section Soft expert system for survival prediction we present a review of the solution published by Yuksel et al. [45] and we detail our own proposal for survival prediction. We include the testing results and the performance analysis of our algorithm. Finally, our conclusions are presented in Section Results and discussion.

Fuzzy sets and soft sets: Definitions

From now on, denotes the power set of U. We use the standard terminology for soft sets. The core concept is taken from Molodtsov [3].

Definition 1 Let E be the universal set of parameters. The pair (F, A) is a soft set over U, a universe of objects, when and A ⊆ E.

A fuzzy subset A of a nonempty set S is a function μ: S → [0, 1], where μ(a) represents the degree of membership of a in A when a ∈ S. Let FS(S) denote the set of all fuzzy subsets of S. The characteristic function χ associated with A ⊆ S produces a natural embedding of into FS(S).

Given a universe set U and a parameters set A, a soft set (F, A) over U is often interpreted as a parameterized family of subsets of U. When e ∈ A, F(e) is the set of e-approximate elements of (F, A), or the subset of U approximated by e. This concept has been thoroughly investigated by Feng and Li [59], and by Maji, Biswas and Roy [7] among others. The study of Feng and Li [59] dwells exhaustively on soft equal relations and classes of soft subsets. Maji et al. [7] introduce notions like soft supersets and subsets, soft equalities, or intersection and union of soft sets.

Practical applications commonly use finite sets U and A, and in that case soft sets are represented in tabular form or by means of binary matrices (cf., Yao [60]). Rows represent the alternatives, whereas columns represent the parameters. The values of all cells are noughts and ones. Example 1 provides a particular instance of a soft set in an abbreviated representation.

Example 1 Let U = {u1, …, u5} and A = E = {a1, a2, a3, a4}. Let us define F(a1) = {u1, u2, u3, u4}, F(a2) = ⌀, F(a3) = {u2, u4}, and F(a4) = {u2, u3, u5}. Table 1 expresses the soft set (F, A) in tabular and matrix format.

Table 1

Representations of a soft set.

	a1	a2	a3	a4
u1	1	0	0	0	(10001011100110100001)
u2	1	0	1	1
u3	1	0	0	1
u4	1	0	1	0
u5	0	0	0	1

Tabular and matrix representations of the soft set (F, A) in Example 1.

The design of our expert system is based on soft rules for which we need the following concept from Maji et al. [7].

Definition 2 The intersection of the soft sets (F1, A) and (F2, B) is a soft set (F1, A) ∧ (F2, B), defined as (G, A × B) where G(a, b) = F1(a) ∩ F2(b) for each element (a, b) in A × B.

Soft-set based decision making was launched by Maji, Biswas and Roy [61].

Soft expert system for survival prediction

We now comment on some approaches for the application of soft sets in DM practice in medicine. Various statistical and Artificial Intelligence techniques have been suggested as tools for the analysis of survival rate and surgical risk in lung cancer resections [39, 42, 62, 63] and in other medical fields [64, 65]. Clark et al. [66] review several approaches to the calculation of medical risk, including univariate analysis, additive methods, use of Bayes theorem by The Society of Thoracic Surgeons, logistic regression, and neural networks.

Successful usages of soft computing techniques can be found in numerous references in the field of medicine. Sanchez [67] was the first to use fuzzy techniques in medical diagnosis. Several extensions of the ideas of Sanchez were exported to settings like intuitionistic fuzzy sets [68], intuitionistic fuzzy soft sets [69], or interval-valued fuzzy soft sets [70]. Çelik and Yamak [71] used the theory of fuzzy soft sets in medical practice. Pawlak et al. [72] applied rough sets to the categorization of convalescents of highly selective vagotomy for duodenal ulcer. Slowiński [50] also discussed duodenal ulcer with rough sets to give advise of the suitable therapy for new duodenal ulcer convalescent patients of high selective vagotomy. Stefanowski and Slowiński applied rough set theory in order to pinpoint the most relevant parameters which are connected with the induction of decision rules from medical databases. In [73], these authors calculated a strong positive causal effect of particular pre-therapy attributes by specifying the preciseness with which patients are classified according to their specific recuperation. The use of soft set theory in the diagnosis of risk of prostate cancer by Yuksel et al. [45] has been the main source of inspiration for our ongoing research (see also [46] for updated methodology).

Algorithm for survival analysis

An initial database was developed in a study of 403 patients who underwent major pulmonary resections at the Salamanca University Hospital from 1994 to 2016. We have taken patients with known survival status and a surgical procedure other than pneumonectomy. The configuration of our expert system has been performed with a random subset of the total of the patients. Validation of the model was performed with the remaining patients. Next in this section, we deepen in our algorithm developed for survival prognosis.

The variables used in our study are: (1) age of patient; (2) body mass index (abbr. BMI, expressed in kg/m2); (3) existence of chronic obstructive pulmonary disease (abbr. COPD); (4) forced vital capacity calculated percentage (abbr. FEV1%); (5) approach of the surgery (thoracotomy or video-assisted thoracoscopic surgery, VATS); and (6) presence of complications in the surgery.

Following the suggestions in [45] and [46], the initial data are converted into fuzzy sets which are subsequently converted into soft sets by taking advantage of the natural embedding of fuzzy sets into soft sets (see [3]). Although Yuksel’s paper [45] reduces parameters of the soft sets, we believe that such step is both unnecessary and unjustified. Therefore we skip it. The primary task of our development lies in the construction of soft rules, and their results make it possible to assess the 5-years patient survival. Finally, the survival rate is obtained from the percentage of affinity with each rule.

Algorithm: Application for survival analysis

We describe the Algorithm for survival analysis and, additionally, we give some instances in each phase. Based on the six parameters of a patient operated with lung cancer, the probability of 5-years survival is calculated.

Algorithm Fuzzy soft set expert system

1: Fuzzification of the initial data set with the six variables.

2: The fuzzy sets in Step 1 are converted into soft sets by their natural embedding.

3: The expert system defines soft rules from the application of the intersection operator (Definition 2). The satisfaction of each rule by each patient is calculated.

4: We compute the 5-years survival percentage for every soft rule: for any such rule, we compare the number of survivors with the population of patients. Finally, we compute the probability of 5-year survival of a patient by the maximum of the probabilities of the rules that he/she satisfies.

The steps of our Algorithm are described below. Examples from our data facilitate the understanding of each stage.

Description of Step 1. We fuzzify the input data set of patients. According to medical literature, we define some membership functions for each variable. Figs 3, 4 and 5 show the membership functions for variables age, body mass index, and FEV1%, respectively.

Fig 3

Functions of membership for age variable.

Fig 4

Functions of membership for BMI variable.

Fig 5

Functions of membership for FEV1% variable.

The linguistic variables used in our model are: (1) for age: Y (young), M (middle), and O (old); (2) for BMI: S (slim), R (regular), and O (obese); and (3) for FEV1%: E (end stage), S (severe), M (mild), and R (regular). Binary variables (COPD, approach, and complications) need no fuzzification. The membership degrees of some input variables are instantiated here, where a superindex indicates the number of variable and a subindex represents a linguistic variable:

Description of Step 2. We need to transform the fuzzy sets obtained in the previous step into soft sets.

We convert the fuzzy sets that represent our data into soft sets by their natural embedding. Molodtsov [3] provides the procedure we apply. Note that the universe of the soft sets must be the set [0, 1]. In addition, for our study, we must choose a subset of the infinite set of parameters.

An analysis of the membership functions from Step 1 allows us to make a suitable choice for the collection of parameters. The universe of alternatives in the new soft sets is our sample of patients. We will now show some partial data of our case study. The soft set corresponding to AGE(Y) and AAGE(Y) is . For the age variable, an appropriate set of parameters associated with the fuzzy set AGE(Y) would be AAGE(Y) = {0.16, 0.49, 0.83}. The ages of some patients are as follows: u1 is 60 years old, u7 is 63 y.o., and u2 is 66 y.o. For these cases, the sets of e-approximate elements of our soft set (F, A) verify the following statements: (1) F(e1) has 64 elements (patients) and includes, among others, u1, u7, and u2; (2) F(e2) has 49 elements and includes, among others, u1 and u7; and (3) F(e3) has 31 elements and includes, among others, u1.

Description of Step 3. The intersection operator allows us to define the soft rules from the soft sets obtained in the previous step. All soft rules are constructed by a combination of the soft sets for each data variable and all possible combinations of a linguistic variable versus an element e. For our data, a total of 10, 368 soft rules have been generated. The next task is to determine the patients who verify each of the rules. Below we show one of the soft rules: where COPD(N) indicates that these patients have not chronic obstructive pulmonary disease, APPR(V) indicates that video-assisted thoracoscopic surgery was performed, and COMPL(N) indicates that there were no surgical complications. Table 2 shows the values of patients who verify this rule.

Table 2

Data of patients who verify the rule defined at Eq 1.

no.	age	BMI	COPD	FEV1%	appr.	compl.	survival
28	71	29.04	0	117.96	2	0	1
51	67	22.86	0	104.78	2	0	1
53	74	21.87	0	109.75	2	0	1
60	73	26.60	0	102.54	2	0	0
62	72	26.84	0	94.82	2	0	1
63	74	24.91	0	122.00	2	0	1
74	69	32.56	0	92.37	2	0	1
92	75	26.17	0	112.43	2	0	0
95	67	23.87	0	89.37	2	0	1
96	73	25.47	0	127.03	2	0	0
97	72	23.68	0	128.77	2	0	1
99	73	28.07	0	84.64	2	0	0

Figs 6 and 7 display the distribution of variables BMI and FEV1% in 5-year non-survival/survival cases for three groups of patients: training set, testing set, and total set of patients. These plots show that the individual variables of a patient are not sufficient for a correct assignment of 5-year survival status.

Fig 6

Distribution of variable BMI in non-survival/survival cases.

Fig 7

Distribution of variable FEV1% in non-survival/survival cases.

Description of Step 4. We are ready to associate a percentage of survival cases to each rule from the results of the previous step. Given a soft rule, we calculate the proportion of patients who survive 5 years out of the total of patients who verify the rule. To put an example, the rule defined at Eq 1 achieves the survival rate 66.67%. This is because eight out of the twelve patients listed survived five years.

Finally, on the grounds of the six parameters of a patient operated with lung cancer, the probability of 5-year survival is computed by the maximum of the probabilities of the rules that he/she satisfies.

Results and discussion

The evaluation of surgical risk in patients undertaking pulmonary resection is a primary target for thoracic surgeons [62]. Lung cancer survival is influenced by many factors, despite the relevance of anatomical extension of the neoplasm, many others could be involved in the prognosis. On the other hand, altered metabolic pathways have a decisive influence on the initiation and progression of cancer. Probably in the near future we can include as part of the variables, metabolic factors like pleiotropic actions of peroxisome proliferator activated receptors related to cancer activation and progression [74, 75]. The influence of these factors has to be weighed according to our ongoing results.

The illness severity and prognostic stratification after major surgery have been established through numerous scoring indices. However, there are few bibliographical references on these indices in the field of thoracic surgery [36, 76]. Harpole et al. [42] identify risk factors similar to ours, but related to morbidity and 30-day mortality for patients who went through major pulmonary resections.

The study of risk in coronary surgeries is also of interest, for example, Ferguson et al. analyze the risk factor of patients undergoing isolated coronary artery bypass grafting [77]. Kurki et al. [78] studied the performance of different preoperative risk models (EuroSCORE, CABDEAL, and Cleveland models) in the prediction of postoperative morbidity and mortality in coronary artery bypass surgery. The principal preoperative risk factors observed are comparable to those found in most of the previously published risk studies.

Licker and other authors [36, 79] recall the following reasons that justify the importance of the knowledge of risk factors for long-term survival in patients affected by lung cancer: (1) the medical staff can assess the risk of a surgical intervention against the mortality risk from an untreated or partially resected lesion, keeping in mind that the only cure option in NSCLC is surgery; (2) patients with a high estimated cardiopulmonary risk might deserve aggressive perioperative medical management including preoperative respiratory training, cardiovascular monitoring with transesophageal echocardiography and right heart catheterization, as well as a planned postoperative admission in an Intensive Care Unit and High Dependency Unit (ICU/HDU). As an alternative to those high-risk cases, the medical team may recommend non-surgical alternatives or lesser invasive surgeries; (3) postoperative outcome data and assessment of their risk factor allow continued improvement of quality control management in a particular hospital and comparison of therapeutic strategies with other medical centers.

In our study, we propose an expert system for the estimation of 5-years survival rate in lung cancer patients undertaking pulmonary resection. We have used a new criterion based on soft sets, which is part of the emerging area of Soft Computing, which is very appropriate for handling inaccurate or uncertain information that we often meet in the available data. The model receives six input variables (age, body mass index, chronic obstructive pulmonary disease, forced vital capacity calculated percentage FEV1%, approach of the surgery, and complications in the surgery) and generates the 5-years survival rate for the expert system output. The risk model is evaluated by a group of patients from thoracic surgeries. The correct classification of 5-years survival is achieved with an accuracy of 79.0%. The application performance results remain consistent with regard to similar documents published before. As in others surgical specialties, elderly patients with lung cancer should be treated according to their physical health and preferences and it is absolutely necessary to overcome the mental bias of not treating elderly population because they more fragile or they have lower life expectancy than their younger peers [80, 81].

An initial database is made up of 403 patients undergoing lung resection, but we have used only 170 patients with known survival status and a surgical procedure other than pneumonectomy. In this research, only patients with a diagnosis of invasive carcinoma were included; all patients with ground-glass opacities (GGO) presented a solid component that was the basis for their staging. We configure our expert system with a hundred patients who were randomly chosen from the total set of available data. The validation process was performed with the rest of the patients.

We validated the performance of categorization of our model with the remainder of the data, which corresponds to the remaining 70 patients in the sample. We have achieved an accuracy of 79.0% in the right classification of each patient in the survival state. Sensitivity and specificity are the most well-known and reputable statistics used to describe a diagnostic test. The sensitivity and specificity for the prediction of survival were 91% and 59% (see Table 3). We computed other statistical measures out of 70 patients: rates of false positive (15.71%) and true positive (55.71%), rates of false negative (5.71%) and true negative (22.85%), precision (0.78), and F1-score (0.84). The development of our model was implemented with the high-performance language Matlab and with the Fuzzy Logic Toolbox [82].

Table 3

Matrix of confusion for our survival test.

		Predicted Survival
		Positive	Negative
Actual Survival	Positive	39	4
	Negative	11	16

We conclude that our novel soft set based criterion is an effective and precise diagnosis application for the determination of 5-years survival rate in lung cancer patients. However, although the results obtained have been good, this study would be enriched by having a database with a greater number of patients and other surgical approaches, so that the expert system would have more information on other situations and on less frequent cases. In a future project, we will include new relevant patient variables (e.g. histological type of cancer, surgical procedure, or variables related to tobacco use) and intend to develop an independent expert system for each type of lung-cancer approach surgery.

Ethics statement

The study protocol was approved by the Salamanca University Hospital and Medical School and Clinical Research Ethics Committee. Written informed consent was obtained from patients or legal guardians before enrolling patients in the study.