Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.

BACKGROUND: Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS: MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS: The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS: Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer.

BACKGROUND: Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs). METHODS: A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set. RESULTS: The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis. CONCLUSION: Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

Transcription factor ELK1 regulates the expression of histone 3 lysine 9 to affect developmental potential of porcine preimplantation embryos.

A series of changes occur in the early embryo that are critical for subsequent development, and the pig is an excellent animal model of human disease, so understanding the regulatory mechanisms of early embryonic development in the pig is of very importance. To find key transcription factors regulating pig early embryonic development, we first profiled the transcriptome of pig early embryos, and confirmed that zygotic gene activation (ZGA) in porcine embryos starts from 4 cell stage. Subsequent enrichment analysis of up-regulated gene motifs during ZGA revealed that the transcription factor ELK1 ranked first. The expression pattern of ELK1 in porcine early embryos was analyzed by immunofluorescence staining and qPCR, and the results showed that the transcript level of ELK1 reached the highest at the 8 cell stage, while the protein level reached the highest at 4 cell stage. To further investigate the effect of ELK1 on early embryo development in pigs, we silenced ELK1 in zygotes and showed that ELK1 silencing significantly reduced cleavage rate, blastocyst rate as well as blastocyst quality. A significant decrease in the expression of the pluripotency gene Oct4 was also observed in blastocysts from the ELK1 silenced group by immunofluorescence staining. Silencing of ELK1 also resulted in decreased H3K9Ac modification and increased H3K9me3 modification at 4 cell stage. To investigate the effect of ELK1 on ZGA, we analyzed transcriptome changes in 4 cell embryos after ELK1 silencing by RNA seq, which revealed that ELK1 silencing resulted in significant differences in the expression of a total of 1953 genes at the 4 cell stage compared with their normal counterparts, including 1106 genes that were significantly upregulated and 847 genes that were significantly downregulated. Through GO and KEGG enrichment, we found that the functions and pathways of down-regulated genes were concentrated in protein synthesis, processing, cell cycle regulation, etc., while the functions of up-regulated genes were focused on aerobic respiration process. In conclusion, this study demonstrates that the transcription factor ELK1 plays an important role in regulation of preimplantation embryo development of pigs and deficiency of ELK1 leads to abnormal epigenetic reprogramming as well as zygotic genome activation, thus adversely affecting embryonic development. This study will provide important reference for the regulation of transcription factors in porcine embryo development.

Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: A multicenter study.

Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment-specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia-induced immunosuppression, a "cold" subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10-feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics-correlated biological pathways were validated externally. Our data suggest that adult-type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Research progress on function and molecular mechanism of transcription factor ELK1 of Ets / 中国生物制品学杂志

@#Ets transcription factor ELK 1,a member of the ternary complex factor(TCF) subfamily in the Ets family,is directly downstream signal of MAPK/ERK pathway and is activated by phosphorylation to execute the function of ERK signal.ELK1,which is highly expressed and phosphorylated in stem cells and tumor cells,plays a role in promoting proliferation,inhibiting apoptosis and differentiation in stem cells.In tumor cells,ELK1 has shown to promote proliferation,migration,and inhibit apoptosis.In neural cells,ELK1 is involved in long-term memory,learning,addiction and other functions.In this paper,the research progress on the main structure and basic biological characteristics of ELK1,the function and mechanism of ELK 1 in tumor cells,stem cells and nerve cells are reviewed in order to provide new ideas for the follow-up research.

ELF4 is critical to zygotic gene activation and epigenetic reprogramming during early embryonic development in pigs.

Zygotic gene activation (ZGA) and epigenetic reprogramming are critical in early embryonic development in mammals, and transcription factors are involved in regulating these events. However, the effects of ELF4 on porcine embryonic development remain unclear. In this study, the expression of ELF4 was detected in early porcine embryos and different tissues. By knocking down ELF4, the changes of H3K9me3 modification, DNA methylation and ZGA-related genes were analyzed. Our results showed that ELF4 was expressed at all stages of early porcine embryos fertilized in vitro (IVF), with the highest expression level at the 8-cell stage. The embryonic developmental competency and blastocyst quality decreased after ELF4 knockdown (20.70% control vs. 17.49% si-scramble vs. 2.40% si-ELF4; p < 0.001). Knockdown of ELF4 induced DNA damage at the 4-cell stage. Interfering with ELF4 resulted in abnormal increases in H3K9me3 and DNA methylation levels at the 4-cell stage and inhibited the expression of genes related to ZGA. These results suggest that ELF4 affects ZGA and embryonic development competency in porcine embryos by maintaining genome integrity and regulating dynamic changes of H3K9me3 and DNA methylation, and correctly activating ZGA-related genes to promote epigenetic reprogramming. These results provide a theoretical basis for further studies on the regulatory mechanisms of ELF4 in porcine embryos.

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.

BACKGROUND: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. METHODS: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). FINDINGS: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). INTERPRETATION: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma.

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways.

OBJECTIVES: To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data. METHODS: In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37). RESULTS: The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort. CONCLUSIONS: CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis. KEY POINTS: • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma.

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer.

PURPOSE: To develop a radiomics signature using diffusion-weighted imaging (DWI) for predicting progression-free survival (PFS) in muscle-invasive bladder cancer (MIBC) patients and to assess its incremental value over traditional staging system. METHOD: 210 MIBC patients undergoing preoperative DWI were enrolled. A radiomics signature was built using LASSO model. A radiomics nomogram was generated to assess the incremental value of the radiomics signature over existing risk factors in PFS estimation in terms of calibration, discrimination, reclassification and clinical usefulness. Kaplan-Meier analysis was used to assess the association of the radiomics signature with PFS. C-index was used as a discrimination measure. Net reclassification improvement (NRI) was calculated to evaluate the usefulness improvement added by the radiomics signature. Decision curve analysis was performed to evaluate the clinical usefulness of the nomograms. RESULTS: The radiomics signature was significantly associated with PFS (log-rank P = 0.0073) and was independent with clinicopathological factors (P = 0.0004). The radiomics nomogram achieved better performance in PFS prediction (C-index: 0.702, 95 % confidence interval [CI]: 0.602, 0.802) than either clinicopathological nomogram (C-index: 0.682, 95 % CI: 0.575, 0.788) or radiomics signature (C-index: 0.612, 95 % CI: 0.493, 0.731), and achieved better calibration and classification (NRI: 0.226, 95 % CI: 0.016, 0.415, P = 0.038). Decision curve analysis demonstrated the better clinical usefulness of the radiomics nomogram. CONCLUSIONS: The DWI-based radiomics signature was an independent predictor of PFS in MIBC patients. Combining the radiomics signature, clinical staging and other clinicopathological factors achieved better performance in individual PFS prediction.

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region.

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models. Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma.

PURPOSE: Isocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI). METHODS: In this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non-enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all-relevant feature selection and a random forest classification for predicting IDH1. Four single-region models and a model combining all-region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients). RESULTS: Among the four single-region radiomics models, the edema model achieved the best accuracy of 96% and the best F1-score of 0.75 while the non-enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor-core model (accuracy 0.96, AUC 0.86 and F1-score 0.75) and the whole-tumor model (accuracy 0.96, AUC 0.88 and F1-score 0.75) was slightly better than the single-regional models. The 8-feature all-region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1-score 0.78. Among all models, the model combining all-region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1-score 0.84. CONCLUSIONS: The radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all-region features performed better than the single-region models, while combining age with all-region features achieved the best performance.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.