CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma

Rationale and Objectives

To develop and validate a computed tomography-based radiomics signature for preoperatively discriminating high-grade from low-grade colorectal adenocarcinoma (CRAC).

Materials and Methods

This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. This study enrolled 366 patients with CRAC (training dataset: n = 222, validation dataset: n = 144) from January 2008 to August 2015. A radiomics signature was developed with the least absolute shrinkage and selection operator method in training dataset. Mann-Whitney U test was applied to explore the correlation between radiomics signature and histologic grade. The discriminative power of radiomics signature was investigated with the receiver operating characteristics curve. An independent validation dataset was used to confirm the predictive performance. We further performed a stratified analysis to validate the predictive performance of radiomics signature in colon adenocarcinoma and rectal adenocarcinoma.

Results

The radiomics signature demonstrated discriminative performance for high-grade and low-grade CRAC, with an area under the curve of 0.812 (95% confidence interval [CI]: 0.749–0.874) in training dataset and 0.735 (95%CI: 0.644–0.826) in validation dataset. Stratified analysis demonstrated that radiomics signature also showed distinguishing ability for histologic grade in both colon adenocarcinoma and rectal adenocarcinoma, with area under the curve of 0.725 (95%CI: 0.653–0.797) and 0.895 (95%CI: 0.838–0.952), respectively.

Conclusions

We developed and validated a radiomics signature as a complementary tool to differentiate high-grade from low-grade CRAC preoperatively, which may make contribution to personalized treatment.

Introduction

Colorectal cancer (CRC) is one of the most common cancers globally, ranking the third and fourth as a cause of cancer-related death in women and men, respectively . Colorectal adenocarcinoma (CRAC) is the most common histologic type, accounting for more than 90% of CRC . The degree of differentiation of CRAC was demonstrated by numerous studies as a significant factor for prognosis . A two-tiered classification system of histologic grade (low grade = well and moderate differentiation; high grade = poor differentiation) was recommended in CRAC for better reproducibility and prognostic significance . Specifically, high-grade CRAC had higher risk to relapse after tumor resection, resulting in poorer prognosis . Preoperatively, radiotherapy or chemoradiotherapy could improve local control and disease-free survival in high-risk patients with rectal cancers . However, neoadjuvant therapies also have been reported for serious side effects, such as occurrence of second cancers and adverse effects on anorectal function . Thus, to achieve maximum benefit and avoid unnecessary side effects caused by preoperatively excessive treatment, discriminating high-grade from low-grade CRAC to assist in identifying patients with high risk of recurrence before treatment would be vital for individual therapy and hence improving outcomes.

To preoperatively evaluate histologic grade, pathologic assessment of biopsy samples could offer some information of tumor differentiation, because colonoscopic biopsy is regularly used to diagnose CRC . However, as tumors were heterogeneous in space , the discrepancy may occur between biopsy and surgical specimen for evaluating histologic grade, because biopsy samples may be superficial, inadequate, or poorly oriented . Additionally, colonoscopic biopsy as an invasive examination has related side effects .

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Materials and Methods

Patients

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Assessment of Histologic Grade

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Acquisition of CT Image

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Radiomics Feature Extraction

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Statistical Analysis

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Radiomics Feature Selection and Radiomics Signature Building

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Predictive Performance of Radiomics Signature

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Stratified Analysis for Radiomics Signature in Colon and Rectal Adenocarcinoma

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Results

Clinical Characteristics

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TABLE 1

Characteristics of Patients in the Training and Validation Datasets

Training Dataset Validation Dataset Characteristics Low Grade High Grade_P_ † Low Grade High Grade_P_ † Age, mean ± SD, y 61.26 ± 13.33 59.55 ± 13.19 .369 60.35 ± 11.82 63.54 ± 12.01 .132 Gender, No. (%) Male 91 (61.5) 44 (59.5) .771 55 (57.3) 29 (60.4) .720 Female 57 (38.5) 30 (40.5) 41 (42.7) 19 (39.6) CT-reported tumor location Right-sided colon 24 (16.2) 29 (38.2) <.001 12 (12.5) 18 (37.5) <.001 Others \* 124 (83.8) 45 (61.8) 84 (87.5) 30 (62.5) CEA level, No. (%) Normal 85 (57.4) 46 (62.2) .499 61 (63.5) 30 (62.5) .903 Abnormal 63 (42.6) 28 (37.8) 35 (36.5) 18 (37.5) Radiomics score median (interquartile range) −1.548 (−2.348 to −0.680) 0.333 (−0.811 to 1.008) <.001 −1.594 (−2.432 to −0.881) −0.118 (−1.476 to 1.086) <.001

Abbreviations: CEA, carcinoembryonic antigen; CT, computed tomography; SD, standard deviation; No. (%), the numbers before parentheses represent the actual numbers and the numbers within parentheses represent corresponding percentages.

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Statistical Analysis of Pathologic Findings of Biopsy

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TABLE 2

The Pathologic Findings of Colonoscopic Biopsy

Training Dataset Validation Dataset Pathologic Findings of Biopsy, No. (%) Low Grade High Grade Low Grade High Grade Low-grade CRAC 67 (45.3) 22 (29.7) 43 (44.8) 11 (22.9) High-grade CRAC 1 (0.7) 13 (17.6) 1 (1.0) 19 (39.6) CRAC without grade \* 56 (37.8) 21 (28.4) 40 (41.7) 14 (29.2) Non-cancer † 5 (3.4) 2 (2.7) 1 (1.0) 2 (4.2) Without a biopsy 19 (12.8) 16 (21.6) 11 (11.5) 2 (4.2)

Abbreviations: CRAC, colorectal adenocarcinoma; No. (%), the numbers before parentheses represent the actual numbers and the numbers within parentheses represent corresponding percentages.

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Reproducibility of Radiomics Feature Extraction

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Radiomics Feature Selection and Radiomics Signature Building

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Predictive Performance of Radiomics Signature

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TABLE 3

Predictive Performance of Radiomics Signature

Predictive Performance AUC (95%CI) SEN SPE PPV NPV Accuracy Training dataset 0.812 (0.749–0.874) 0.635 0.845 0.671 0.822 0.775 Validation dataset 0.735 (0.644–0.826) 0.521 0.854 0.641 0.781 0.743 Rectal adenocarcinoma 0.895 (0.838–0.952) 0.789 0.821 0.600 0.920 0.813 Colon adenocarcinoma 0.725 (0.653–0.797) 0.500 0.871 0.712 0.732 0.727

Abbreviations: AUC, area under curve; CI, confidence interval; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value.

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Stratified Analysis for Radiomics Signature in Colon and Rectal Adenocarcinoma

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Discussion

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Conclusion

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Acknowledgments

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Appendix

Appendix A.1

Radiomics Feature Extraction Methodology

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Energy=∑Ni=1X(i)2 Energy

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Kurtosis=1N∑Ni=1(X(i)−X¯¯¯)4(1N∑Ni=1(X(i)−X¯¯¯)2)2 Kurtosis

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Skewness=1N∑Ni=1(X(i)−X¯¯¯)31N∑Ni=1(X(i)−X¯¯¯)2√3 Skewness

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Mean=1N∑Ni=1X(i) Mean

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MAD=∑Ni=1∣∣X(i)−X¯¯¯∣∣N MAD

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RMS=∑Ni=1X(i)2N−−−−−−−√ RMS

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SD=1N−1∑Ni=1(X(i)−X¯¯¯)2−−−−−−−−−−−−−−−−−−−√ SD

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Variance=1N−1∑Ni=1(X(i)−X¯¯¯)2 Variance

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Entropy=−∑N1i=1P(i)log2P(i) Entropy

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log

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Uniformity=∑N1i=1P(i)2 Uniformity

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Compactness1=Vπ√A23 Compactness

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Compactness2=36πV2A3 Compactness

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sph_dis=A4πR2 sph_dis

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Sphericity=π13(6V)23A Sphericity

(

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svr=AV svr

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(1) Get Radiology Tree app to read full this article<

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Autocorrelation=∑Ngi=1∑Ngj=1ijP(i,j) Autocorrelation

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Clu_pro=∑Ngi=1∑Ngj=1[i+j−μx(i)−μy(j)]4P(i,j) Clu_pro

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Clu_shade=∑Ngi=1∑Ngj=1[i+j−μx(i)−μy(j)]3P(i,j) Clu_shade

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Clu_ten=∑Ngi=1∑Ngj=1[i+j−μx(i)−μy(j)]2P(i,j) Clu_ten

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Correlation=∑Ngi=1∑Ngj=1ijP(i,j)−μi(i)μj(j)σx(i)σy(j) Correlation

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Diff_entropy=∑Ng−1i=0Px−y(i)log2[Px−y(i)] Diff_entropy

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Dissimilarity=∑Ngi=1∑Ngj=1|i−j|P(i,j) Dissimilarity

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Energy=∑Ngi=1∑Ngj=1[P(i,j)]2 Energy

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Entropy=−∑Ngi=1∑Ngj=1P(i,j)log2[P(i,j)] Entropy

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Homogeneity2=∑Ngi=1∑Ngj=1P(i,j)1+|i−j|2 Homogeneity

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IMC1=HXY−HXY1max{HX,HY} IMC

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{

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IMC2=1−e−2|HXY2−HXY|−−−−−−−−−−−−−−−√ IMC

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IDMN=∑Ngi=1∑Ngj=1P(i,j)1+|i−j|2Ng2 IDMN

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IDN=∑Ngi=1∑Ngj=1P(i,j)1+|i−j|2Ng IDN

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inv_var=∑Ngi=1∑Ngj=1P(i,j)|i−j|2,(i≠j) inv_var

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sum_average=∑2Ngi=2[iPx+y(i)] sum_average

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sum_entropy=−∑2Ngi=2Px+y(i)log2[Px+y(i)] sum_entropy

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sum_var=∑2Ngi=2(i−SE)2Px+y(i) sum_var

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variance=∑Ngi=1∑Ngj=1(i−μ)2P(i,j) variance

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SRE=∑Ngi=1∑Nrj=1[p(i,j|θ)j2]∑Ngi=1∑Nrj=1[p(i,j|θ)] SRE

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LRE=∑Ngi=1∑Nrj=1j2p(i,j|θ)∑Ngi=1∑Nrj=1[p(i,j|θ)] LRE

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GLN=∑Ngi=1[∑Nrj=1p(i,j|θ)]2∑Ngi=1∑Nrj=1[p(i,j|θ)] GLN

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RLN=∑Nrj=1[∑Ngi=1p(i,j|θ)]2∑Ngi=1∑Nrj=1[p(i,j|θ)] RLN

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RP=∑Ngi=1∑Nrj=1p(i,j|θ)Np RP

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LGLRE=∑Ngi=1∑Nrj=1[p(i,j|θ)i2]∑Ngi=1∑Nrj=1[p(i,j|θ)] LGLRE

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HGLRE=∑Ngi=1∑Nrj=1i2p(i,j|θ)∑Ngi=1∑Nrj=1[p(i,j|θ)] HGLRE

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SRLGLE=∑Ngi=1∑Nrj=1[p(i,j|θ)i2j2]∑Ngi=1∑Nrj=1[p(i,j|θ)] SRLGLE

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SRHGLE=∑Ngi=1∑Nrj=1[p(i,j|θ)i2j2]∑Ngi=1∑Nrj=1[p(i,j|θ)] SRHGLE

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LRLGLE=∑Ngi=1∑Nrj=1[p(i,j|θ)j2i2]∑Ngi=1∑Nrj=1[p(i,j|θ)] LRLGLE

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LRHGLE=∑Ngi=1∑Nrj=1p(i,j|θ)i2j2∑Ngi=1∑Nrj=1[p(i,j|θ)] LRHGLE

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TABLE A.1

Representing Number of Functions on X, Y, Z Scale

Functions on X, Y, Z scale LLL LLH LHL LHH HLL HLH HHL HHH Representing number 1 2 3 4 5 6 7 8

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Appendix A.2

Statistical Analysis

Appendix A2a

Demographic comparison between patients with low-grade and high-grade CRAC and between training and validation datasets

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Appendix A2b

The packages of R software used for statistical analysis

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Appendix A.3

Feature Normalization

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X′=X−X¯¯¯SD X

′

−

where X is the value of each selected feature in a patient, whereas X′ X

′ is the corresponding normalized value. X¯¯¯ X

¯ represents the mean of the values of the feature, and SD is standard deviation in training dataset. The mean and standard deviation of each features are shown in Table A.2 .

TABLE A.2

Data of z-Score Normalization

Features Mean Standard Deviation db3_4_RMS 1996.043 590.3509 db10_4_RMS 1966.537 577.5884 db10_8_range 469.7477 107.6682 bior3.7_3_energy 9933893 5400296 bior4.4_8_sd 42.64378 11.39670 bior5.5_3_sd 60.44354 17.05864 bior5.5_4_RMS 1990.187 589.9640 bior5.5_5_sd 287.2846 58.48172 dmey_2_sd 63.40118 16.19524 dmey_3_energy 9575864 5159117 dmey_4_sd 44.33686 11.35308 rbio2.2_1_inv_var 0.3163109 0.06468699 rbio2.8_4_sd 49.22477 12.79117 rbio3.9_3_energy 9820202 5332593

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Appendix A.4

Rad-Score Calculation Formula

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CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma

Rationale and Objectives

Materials and Methods

Results

Conclusions

Introduction

Materials and Methods

Patients

Assessment of Histologic Grade

Acquisition of CT Image

Radiomics Feature Extraction

Statistical Analysis

Radiomics Feature Selection and Radiomics Signature Building

Predictive Performance of Radiomics Signature

Stratified Analysis for Radiomics Signature in Colon and Rectal Adenocarcinoma

Results

Clinical Characteristics

Statistical Analysis of Pathologic Findings of Biopsy

Reproducibility of Radiomics Feature Extraction

Radiomics Feature Selection and Radiomics Signature Building

Predictive Performance of Radiomics Signature

Stratified Analysis for Radiomics Signature in Colon and Rectal Adenocarcinoma

Discussion

Conclusion

Acknowledgments

Appendix

Appendix A.1

Radiomics Feature Extraction Methodology

Appendix A.2

Statistical Analysis

Appendix A2a

Demographic comparison between patients with low-grade and high-grade CRAC and between training and validation datasets

Appendix A2b

The packages of R software used for statistical analysis

Appendix A.3

Feature Normalization

Appendix A.4

Rad-Score Calculation Formula

References

Further Reading

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Building a Tree From the Leaves Down A Challenge for Contemporary Educators

Characterization of Urinary Stone Composition by Use of Whole-body, Photon-counting Detector CT