OBJECTIVE: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. METHODS: this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. RESULTS: we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. CONCLUSION: the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. SIGNIFICANCE: the proposed method is general and more effective radiomic feature selection strategy.
OBJECTIVE: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. METHODS: this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. RESULTS: we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. CONCLUSION: the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. SIGNIFICANCE: the proposed method is general and more effective radiomic feature selection strategy.
Authors: Philippe Lambin; Ralph T H Leijenaar; Timo M Deist; Jurgen Peerlings; Evelyn E C de Jong; Janita van Timmeren; Sebastian Sanduleanu; Ruben T H M Larue; Aniek J G Even; Arthur Jochems; Yvonka van Wijk; Henry Woodruff; Johan van Soest; Tim Lustberg; Erik Roelofs; Wouter van Elmpt; Andre Dekker; Felix M Mottaghy; Joachim E Wildberger; Sean Walsh Journal: Nat Rev Clin Oncol Date: 2017-10-04 Impact factor: 66.675
Authors: Pol Cirujeda; Yashin Dicente Cid; Henning Muller; Daniel Rubin; Todd A Aguilera; Billy W Loo; Maximilian Diehn; Xavier Binefa; Adrien Depeursinge Journal: IEEE Trans Med Imaging Date: 2016-07-18 Impact factor: 10.048
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919