Accurately predicting the status of 1p/19q is of great significance for formulating treatment plans and evaluating the prognosis of gliomas. Although there are some works which can predict the status of 1p/19q accurately based on magnetic resonance images and machine learning methods, they require to delineate the tumor contour preliminarily, which cannot satisfy the needs of computer-aided diagnosis. To deal with this issue, this work proposes a novel deep multi-scale invariant features-based network (DMIF-Net) for predicting 1p/19q status in glioma. Firstly, it uses the wavelet-scattering network to extract multi-scale and multi-orientation invariant features, and deep split and aggregation network to extract semantic features. Then, it reduces the feature dimensions using a multi-scale pooling module and fuses these features with concatenation. Finally, with inputting the bounding box of the tumor region it can predict the 1p/19q status accurately. The experimental results illustrate that, without requiring to delineate the tumor region accurately, the AUC predicted by DMIF-Net can reach 0.92 (95%CI=[0.91, 0.94]). Compared with the best deep learning model, the AUC, sensitivity, and specificity increased by 4.1%, 4.6%, and 3.4%, respectively. Compared with the state-of-the-art models on glioma, AUC and accuracy have increased by 4.9% and 5.5%, respectively. Moreover, the ablation experiments demonstrate that the proposed multi-scale invariant feature extraction module can promote effectively the 1p/19q prediction performance, which verify that combining the semantic and multi-scale invariant features can significantly increase the prediction accuracy for 1p/19q status without knowing the boundaries of tumor region, providing therefore an auxiliary means for formulating personalized treatment plan for low-grade glioma.