Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis

Transcription

1 Non-sall cell lung cancer: quantitative phenotpic analsis of CT iages as a potential arker of prognosis Jiangdian Song PhD Zaii Liu MD PhD Wenzhao Zhong MD PhD Yanqi Huang MD Zelan Ma MD Di Dong PhD Changhong Liang MD PhD Jie Tian PhD Appendi A1 A detailed flowchart of this stud is presented in Suppleentar Fig. S1. The trial of clinical prediction and prognosis were perfored successivel according to the features etracted fro CT iages. Segentation results (GGO solid and cavit) were presented in Suppleentar Fig. S. In our Radioics approach 59 phenotpic descriptors were etracted fro the pre-therap CT iages ainl 3D teture Gabor and wavelet features. The construction of feature set was presented in Suppleentar Fig. S3. Based on the SVM the features were scored and the highest scored were selected for prediction and following overall survival analsis of NSCLC. Here we presented the radioics features at the top of the score list which were divided into four groups: Teture Gabor Shape and Wavelet as described in the anuscript. We introduced the coputational forulas of these features in detail in the following paragraphs. In the ain tet the nuber reported behind each feature in Table 3 denoted the iage after wavelet transfor the nuber of 1 3 and 4 represents the LL LH HL and HH iage respectivel. In addition the nuber of [] in gra-level co-occurrence atri: denotes the LL LH HL and HH iage represents the different directions: 1 3 and 4 denote the direction of and 135. The construction of our feature set is described in Suppleentar Fig. S3. Suppleentar Fig. S1. Flowchart of this stud: the section of ROI segentation feature etraction and patient

2 enrollent. The clinical prediction and prognosis are perfored successivel according to the phenotpic features. Suppleentar Fig. S. (a) The segented result of a ground glass opacit (green line labelled) in cross section. non-enhanced CT iage in a 5-ear-old an with NSCLC (stage TN0M0). (b) The segented result of a ground glass opacit (green line labelled) in cross section. non-enhanced CT iage in a 65-ear-old an with NSCLC (stage T1N0M0). (c) The segented result of squaous cell carcinoa (green line labelled) in cross section. Contrast-enhanced CT iage in a 61-ear-old an with NSCLC (stage TaNM0). (d) The segented result of squaous cell carcinoa with cavit (green line labelled) in cross section. Contrast-enhanced CT iage in a 60-ear-old an with NSCLC (stage T1N0M0). Suppleentar Fig. S3. Construction of feature set. The radiographic feature set includes 3D features Teture features Gabor features and Wavelet features. Group 1: Teture Run-length is a etrics to quantif gra level runs in an iage. Since the consecutive piels that have the sae gra level value in one direction could be easured the gra level run is defined as the length in nuber of piels. In a gra level run length atri M ( i j θ ) the ( i j) th eleent describes the nuber of ties the j gra level i appears consecutivel in the direction specified b θ. The other definitions are described as follow:

3 M ( i j θ ) is the ( i j) th point in the given run-length atri M for a direction θ Ng ( ) is the nuber of discrete intensit values in the iage Nr () is the nuber of different run lengths N( p ) is the nuber of voels in the iage. (1) Long Run Ephasis of RL and RL3 LRE= M ( i j ) j N ( g) N ( r) i1 j1 i1 j1 M ( i j ) () Long Run Low Gra Level Ephasis of RL and RL3 LRLGLE= i1 j1 i1 j1 M ( i j ) j [ ] i M ( i j ) (3) Long Run High Gra Level Ephasis of RL and RL3 LRHGLE= M ( i j ) j i N ( g) N ( r) i1 j1 N ( g) N ( r) i1 j1 M ( i j ) (4) Short Run Ephasis of RL3 SRE= i1 j1 i1 j1 M ( i j ) [ ] j M ( i j ) (5) Energ of RL1 and RL3 Energ= i j [ M( i j )] N ( g) N ( g) 1 1 The gra-level co-occurrence atri is defined as M( i j D ) a atri to describe the gra level distribution b a distance of D piels in direction of an iage with the size of N( g) N( g) where the ( i j) th eleent represents the nuber of ties the cobination of intensit levels occurs in two piels in the iage. The other definitions are described as follows: M ( i j ) is the co-occurrence atri b the D and Ng ( ) is the nuber of discrete intensit levels in the iage is the ean of M ( i j )

4 is the ean of is the ean of is the standard deviation of is the standard deviation of (6) Correlation of CO[] and CO[33] (7) Contrast of CO[11] (8) Variance of CO[1] Correlation N ( g) N ( g ) i j i j 1 1 ijm ( i j) ( i) ( j) ( i) ( i) N ( g) N ( g) Contrast i1 j1 i j M ( i j) N ( g) N ( g) Variance i1 j1 i M ( i j) Group : Gabor Gabor filter naed after Dennis Gabor is a linear filter used for edge detection which is usuall used in the field of face recognition. It could select valuable iage inforation in different directions and different scales. Therefore the visual characteristics of the lung lesion could be well described b Gabor filters. We used eight directions and five scales to etract Gabor features. Mean variance and entrop were used to construct the Gabor feature group. The nuber of [] in Gabor Feature: eans the LL LH HL and HH iage. Gabor agnitude teture representation (GMTR) and Gabor phase-based teture representation (GPTR) are captured using the convolution between ulti-scale and ulti-directional Gabor wavelet function and the eans the feature we selected on the iage in one direction and one scale. Here we use eight directions and five scales so the nuber of iages after Gabor transfor is 80 (40 GMTR and 40 GPTR). Nl indicates the length of histogra of gra-level of Gabor iage and the Pi denotes the nuber of gra level i N indicates the su of iage piels X i presents the intensit of i on the Gabor iage. (9) Mean of Gabor (10) Variance of Gabor (11) Entrop of Gabor i Mean X i N Variance X i Mean N 1 i

5 Nl Entrop Pilog P i i1 Group 3: Shape The shape features present the distribution of voel intensities on the CT iage. The three-diensional tuor iage is X with N voels. V denotes the volue and A is the surface area of the volue of interest. The followings are the coputational forulas of the representative features: (1) Copactness Copactness 36 V 3 A (13) Skewness of HL and LH 3 i1( X ( i) X ) skewness N ( i1( X ( i) X ) ) N 3 (14) Kurtosis of LL and HH 4 i1( X ( i) X ) kurtosis N ( i1( X ( i) X ) ) N (15) Sphericit Appendi A 3 (6 V ) sphericit A The consistenc of the highest scored signatures fro different readers was tested. We used two radiologists with ore than ten ears of eperience in clinical diagnosis to draw the initial seed point for autoatic tuor segentation. The were ainl responsible for seed point choice and their selection of seed points was copletel independent. Manual segentation would be perfored b clinicians once the autoatic segentation results were poor. We randol selected 0 cases and the siilarit inde of features fro different segentation results was presented in Suppleentar Fig. S4. The average stabilit of features fro different segentation results was 9.50%. The inter-class correlation coefficient (ICC) b two observers was ranged fro to

6 Suppleentar Fig. S4. Stabilit of features on 0 tuors b ultiple auto-segentation fro different radiologists. The average stabilit of each feature is higher than 9.50%. Appendi A3 5-fold cross-validation process: K-fold cross-validation is a universal ethod for validation in the field of statistics. In this stud 5-fold cross-validation was used. The original saple was randol partitioned into 5 subsaples. Of the 5 subsaples a single subsaple was retained as the validation data for testing the odel and the reaining 4 subsaples were used as training data. The 5-fold cross-validation process was then repeated 5 ties (the nuber of folds) with each of the 5 subsaples used eactl once as the validation data. The results fro the all the repeat eperients then been averaged to produce a final estiation. The 5-fold cross-validation was a good odel through a lot of eperients using large data sets and different learning techniques. The advantage of this ethod over repeated rando sub-sapling is that all observations are used for both training and validation and each observation is used for validation eactl once. Appendi A4 The response receiver operating characteristic curves (ROC) indicated that the highest scored features selected b SVM could pla a significant role for prediction. We tested the predictive abilit of the top features for N staging (stage N0/N1 vs. N/N3) histopatholog (squaous cell carcinoa vs. adenocarcinoa) and overall clinical stage (stage I/II vs. III/IV). The area under curve (AUC) of ROC of N staging was 0.79 and the curve of histopatholog and overall clinical stage were 0.76 and Suppleentar Fig. S5 was the predictive perforances of the highest scored signatures for histopatholog and clinical TNM staging. And Suppleentar Figure S6 was the ROC curve of histopatholog.

7 Suppleentar Fig. S5. Predictive perforances of the top scored signatures for histopatholog and clinical TNM staging. The curves present the prediction accurac of N staging (stage N0/N1 vs. N/N3) overall clinical stage (stage I/II vs. III/IV) and histopatholog (squaous cell carcinoa vs. adenocarcinoa) b different representative features. Suppleentar Fig. S6. The receiver operating characteristic curve of histopatholog prediction on squaous cell carcinoa vs. adenocarcinoa when using the top scored 5 features. The area under the curve is 0.76.