Author's response to reviews Title:Justified Granulation Aided Noninvasive Liver Fibrosis Classification System Authors: Marcin Bernas (marcin.bernas@us.edu.pl) Tomasz Orczyk (tomasz.orczyk@us.edu.pl) Joanna Musialik (jmusialik@sum.edu.pl) Marek Hartleb (mhartleb@sum.edu.pl) Barbara Blonska Fajfrowska (bbf@sum.edu.pl) Version:7Date:19 June 2015 Author's response to reviews: see over
dr Marcin Bernaś 39 Bedzinska 41-200 Sosnowiec Poland marcin.bernas@us.edu.pl +48 517 435 509 Dear Editorial Board I am pleased to submit an original research article entitled Justified Granulation Aided Noninvasive Liver Fibrosis Classification System by prof. Barbara Błońska Fajfrowska, prof. Marek Hartleb, M.Sc. Tomasz Orczyk, dr Joanna Musialik and dr Marcin Bernas for consideration for publication in the BMC Medical Informatics & Decision Making. The research is a continuation of a project aiming to create an on-line E-medical diagnosis support system for non-invasive liver fibrosis recognition. We previously uncovered that it is possible to estimate liver fibrosis stage, basing on blood tests, however it must be noted that these works were preliminary and performed on small dataset. An advantage of proposed approach over the biopsy is the fact that it does not require hospitalization and can be repeated in regular periods of time without any risk to the patient. The research has been carried out on archival data of patients with hepatitis C who underwent liver biopsy. The data contains the routine laboratory examinations of peripheral blood, such like blood morphology, coagulation, biochemistry and protein electrophoresis and liver biopsy result. These data, in anonymized form, was obtained from Dept. of Gastroenterology and Hepatology, Prof. Kornel Gibiński Central Clinical Hospital of the Silesian Medical University in Katowice with the consent of the Head of the Gastroenterology and Hepatology Unit. In this manuscript, we show that proposed method based on granular computing paradigm is promising and proved to be superior to other researched methods. Moreover, the data as information granules (intervals and fuzzy sets) can be presented graphically (human centric approach), while the conclusion is made by intuitive voting procedure. The proposed solution is robust to missing or corrupted data. We believe that this manuscript is appropriate for publication by the BMC Bioinformatics because it presents novel application of granular computing for blood analysis (morphology, coagulation, biochemistry, protein electrophoresis). Our manuscript creates a comparison model for future studies of liver fibrosis stage evaluation based on blood tests. This manuscript has not been published and is not under consideration for publication elsewhere. We have no conflicts of interest to disclose. Thank you for your consideration, Sincerely, Marcin Bernas, assistant Professor, Institute of Computer Science Faculty of Computer Science and Material Science University of Silesia, Poland
Response to reviewers Initial remarks * line numbering is included in the main text file * The research has been carried out on archival data of patients with hepatitis C who underwent liver biopsy. The data contains the routine laboratory examinations of peripheral blood, such like blood morphology, coagulation, biochemistry and protein electrophoresis and liver biopsy result. These data, in anonymized form, was obtained from Clinic of Gastroenterology, Medical University of Silesia with the consent of the head of the hospital unit. The information was added in the paper. The authors would like to thank Reviewers for careful review and providing us with their comments and suggestion to improve the quality of the manuscript. First review Reviewer 1 Although it is a very interesting research for clinicians, validation of the complicated equations and formulas must be done by specialized mathematicians and/or statisticians before publishing such wonderful conclusions. The equations was verified and corrected according to a mathematician remarks. Needs a lot of grammar and language revision. The text was proofread by authors and translator. Abstract 1. According to the World Health Organization 130 150 million of peoples globally are chronically infected s with hepatitis C virus. this number might be an underestimation, it needs a Reference. We agree that exact value is unknown and it was used to highlight a scale of an issue. The reference of World Health Organization fact sheet was added to the text: http://www.who.int/mediacentre/factsheets/fs164/en/ Method: 1. How 33 different blood attributes collected and 8 of them have been eliminated from the set, leaving 26 of them? How? Is it because age was further eliminated? Explain. The researched data set contains 33 different blood attributes collected from patients. Due to high count of missing values (over 66%) in some attributes, 8 of them have been eliminated from the set, leaving 25 of them for further processing. Eliminated attributes did not have values representation for all stages of liver fibrosis. Therefore, leaving these attributes could lead to underestimation or overestimation of the method. The 25 blood attribute and age was processed by proposed method (26 in total). All processed attributes were described in Table 1. The clarification was added to the paper. A Table. 2. Fiver fibrosis medical data distribution..liver
Misspell was corrected in the text. Reviewer 2 The authors state (on p. 3) that eight of these attributes can be eliminated to leave 26: this should be 25 instead. Misspell was corrected. There are 25 blood attributes. Metavir score is divided into five stages (F1 F5). The authors have only defined three stages of fibrosis, i.e., S1 for F0 and F1, S2 for F2 and F3, and S3 for F4. As a clinician, I find this paper quite difficult to follow. However, the main message is that, using the authors' method, without performing a LB, the error rate in misclassification of the S1 and S3 classes is below 6.5%. This is of major importance as stages 0 and 1 mean almost no liver fibrosis, whereas stage 4 means cirrhosis. Therefore, when a new non-invasive tool is able to accurately predict these stages this is of major improvement in clinical practice. However, for patients who are neither S1 nor S3, i.e. who are S2, a liver biopsy will remain mandatory. Unfortunately, based on analyzed dataset and according to many authors some biopsy fibrosis stages are difficult to diagnose, even for experienced doctors [10]. Therefore, the classes were group to give reliable results. The method however can be used to evaluate the exact fibrosis class in METAVIR scale, however the results should be treated as an advice. The results were added to a manuscript as Table 7 and Table 8. Table. 7. The classification accuracy using 5 classes of fibrosis by METAVIR scale Metavir class (F) Precision (%) Sensitivity (%) 0 88.6 14.2 1 79.5 56.8 2 88.9 22.8 3 93.0 36.9 4 88.0 81.4 Overall accuracy (%): 52.1 Table.8. The confusion matrix presented for 5 class of liver fibrosis classification (METAVIR scale) using the proposed method Biopsy classified as 0 1 2 3 4 0 3 9 5 2 2 1 25 71 12 6 11 2 7 23 13 10 4
3 2 10 14 24 15 4 5 7 3 3 79 The overall accuracy of the method is 52.1 %. The Table 8 shows that using five class classifications the uncertainty of the result must be considered. The majority of misclassification cases are usually made within neighboring classes (111 of all 175). These authors' method was established using laboratory data from 290 patients with chronic hepatitis C and from a single center in Katowice. It is therefore mandatory to validate this methodology in an independent set of chronic hepatitis C patients as well as a set of patients with a chronic liver disease other than hepatitis C virus infection. The research was extended to verify the model against patients with various liver disease etiologies. Therefore, the 365 patients' dataset was used, where the patients with HBV, HCV as well as nonalcoholic/alcoholic hepatitis were included. The result was presented in Table 5 and Table 6. The proposed model proved to be superior over various other classifiers. The results are stable and 67.4% of overall accuracy was achieved. Furthermore, misclassification between the first and the last class decreased to 6.2%. Additional research was added to a text. Quality of written English: Needs some language corrections before being published The text was proofread by authors and translator. Reviewer 3 The article has clearly written statement of objectives and the experimental part appears to be solid work. Unfortunately, the mathematical/ notational part has major deficiencies which need to be corrected before any publication is possible. The equations was verified and corrected according to a mathematician remarks. Here are some major notational problems I noted: page 4, line 47: left and right bound should be linked to granule index, for example using l,k,n in subscripts The all indexes are added to an equation. Now, it is defined as: g k,n,α =G(X k,n, )= [,,,,,,, page 5, lines 1-2: the function definition is formally incorrect, indexes k, n, m should appear in function argument or be bound to a quantifier. The set X k,n was added as a parameter, which contain n,k indexes. The x m value was defined as,,. Now, the equation is defined as:,,,, :,! " # %, & %, (1) ',,,, :, ( ) #, &, where:,,,
, X k,n - set contains values of k th blood attribute of patients with the n th fibrosis class, - median over a set X k,n, - specificity parameter. Also, explain the intuition behind the choice of functions Vr and Vl, perhaps use a drawing for illustration. The intuitive character of V family function was illustrated in Figure 3/ Figure4. Figure 3 The illustration of X k,n set for given α in value domain The functions V l and V r favor the border values of a set for =0 to values close to median for = (Fig. 4). In practice (Fig. 3), for values =(0, ), the V function family assumes maximal values in proximity of local concentration of elements of a set X k, n. These values can be treated as characteristic representation of a set for given. page 5, line 13: the notation is formally incorrect. I would suggest using proper set notation along with MIN, MAX functions over the sets, if required. The function was corrected and rewritten using argmax function: g k,n,α = G(X k,n )= ] (2) page 6, line 16: explain why trapezoidal fuzzy set was used? The trapezoidal fuzzy membership function was selected as intuitive fuzzy representation of two intervals. Moreover, this sets representation simplifies the calculations and was successfully applied in many medical works [28, 29]. Finally, the initial experiments with other fuzzy representation e.g. Gaussian, triangle, bell-shape did not increase the model accuracy. The remark was added to a text.
page 6, line 19: the notation is incorrect for the definitions of a, b, c, and d. Use proper set notation and make sure that all free indexes appearing on the right hand of each formula also appear in the arguments, for example for a, it can be something like a(k,n,g) The equation was rewritten according to remarks. The granulation function G,, which constructs fuzzy granule and its membership function, was defined as follows: G,: -g /,0,12, g /,0,13 4 g6 /,0,12, 1 3, g6 /,0,12,1 3 g6 /,0,8,13 b, b, b :, b ;, where: b inf g /,0,8 a,/,0,8, b inf g /,0,13 a,/,0,13, b : sup g /,0,13 a,/,0,13, b ; sup g /,0,8 a,/,0,8. : x ) b R U QR S QT 1 & d and x! b MR W XTQP μ F6G,H,2,I3 x, d : x " b R N W XTQR ; 1 Z d and x ( b : Y M 1 : x ) b and x " b : L 0 : otherwise where: O PQR SQT k- identifies blood and age attribute, n- liver fibrosis class, d - generalization parameter d c0,1, α e - j-th α-cut evaluated using Eq. 3, j g0,, z & 1j, α 8 - constatnt equal 0 in equation, z - number of cuts, inf/ sup - lower/upper boundary of interval (g granule). x R, d c0,1,(4) page 7, lines 7-8: please explain the motivation and intuition behind your classification procedure The data of a given patient is compared with the model using membership function k l6m,n,8, o p,, where y k is the medical examination result of the patient for a given k th attribute. The membership function provides information, whether or not patient has n th liver fibrosis class, according to k th attribute. The aggregation (averaging) over all attributes (k) is performed for each, q 0.. r & 1 and n th class separately. The motivation of voting was based on the physician analysis scheme, where similarities of symptoms are analyzed. In this case, the classification procedure weight, how many fuzzy granules favors a given class. page 7, line 24: the notation for w n is incorrect. w n is a function of z, j and K', and these arguments should appear on the left hand side of the definition. The equation was corrected and all arguments are considered within equation. Now, the equation is defined as: s t,u,v w, m ~ y z{m,n,2, o } m,u tq 8 uv, p w
where: n is a number of fibrosis class, ƒ is set of attributes selected for classisification, k is an attribute of the K' set, card is cardinality of a set, z and d are the method parameters. Finally, the patient's fibrosis class ( ) is calculated as maximal value of w function: t,u,v w argmax w,t,ˆ Y, n# 0 g,,:j, (6) where: Y- a set of laboratory blood test results of the patient, n - represent the liver fibrosis class, defined as n=1,..., 3, z, d, K' - parameters of the proposed method. Minor correction: page 3, line 49: replace 26 by 25. The misspell was corrected Second review Reviewer 2 In this revised version of the manuscript of Bernas M et al. the authors have addressed almost all my concerns/questions except one. Their tool should be validated in an independent cohort of liver patient from another institution. The first research was conducted on data from 290 patients with hepatitis C virus collected over 6 years from the Dept. of Gastroenterology and Hepatology, Prof. Kornel Gibiński Central Clinical Hospital of the Silesian Medical University in Katowice. According to reviewer remarks and authors contribution the method was additionally verified against dataset obtained from 365 patients with liver disease of various etiologies. The results are stable and 67.4% of overall accuracy was achieved. According to authors knowledge, there is no open access liver disease database, which contains majority of analyzed parameters set. In future, the analysis will be extended to measure the robustness of the method against independent cohort of liver patient from another institution. Editor remarks 1. Mention all authors specifically in the Authors' Contribution section. The Authors with their contribution was thoroughly described in the section according to requirements 2. Please include an Abbreviations section. The abbreviation section was added
3. Please include in the Acknowledgment section the details of any funding. The Acknowledgment section was added along with funding details 4. Please clarify in the manuscript if the data set is freely available and if not, who gave the authors permission to use it. The data in anonymized form, were obtained from the Dept. of Gastroenterology and Hepatology, Prof. Kornel Gibiński Central Clinical Hospital of the Silesian Medical University in Katowice with the consent of the Head of the Gastroenterology and Hepatology Unit. The information was included into manuscript. 5. Please list the figure legends after the References. The figures legend was added to a manuscript.