To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC).
We enrolled 222 ...patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample
test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation.
The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model.
Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy.
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Objective
To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).
Methods
One hundred ...thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use.
Results
The fusion radiomic signature has significant association with histologic grade (
p
< 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950–0.998) in the training cohort and 0.902 (95% CI 0.798–1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.
Conclusion
We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.
Key Points
• Radiomic signature has strong discriminatory ability for the histologic grade of PNETs.
• Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading.
• The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).
One-hundred and eighteen patients (training cohort:
= 88; validation cohort:
= 30) with ...advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.
The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514;
< 2.68 × 10
). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649;
< 1.60 × 10
). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages.
Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies.
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Abstract
Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can ...benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1–2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control.
We report on the fabrication of photochromic polymersomes exhibiting photoswitchable and reversible bilayer permeability from newly designed poly(ethylene oxide)-b-PSPA (PEO-b-PSPA) diblock ...copolymers, where SPA is spiropyran (SP)-based monomer containing a unique carbamate linkage. Upon self-assembling into polymersomes, SP moieties within vesicle bilayers undergo reversible phototriggered isomerization between hydrophobic spiropyran (SP, λ2 > 450 nm irradiation) and zwitterionic merocyanine (MC, λ1 < 420 nm irradiation) states. For both SP and MC polymersomes, their microstructures are stabilized by multiple cooperative noncovalent interactions including hydrophobic, hydrogen bonding, π–π stacking, and paired electrostatic (zwitterionic) interactions, with the latter two types being exclusive for MC polymersomes. Control experiments using analogous block copolymers of hydrophobic SP monomer with a carbonate linkage (SPO) and conventional spiropyran methacrylate monomer (SPMA) containing a single ester functionality were then conducted, revealing that carbamate-incurred hydrogen bonding interactions in PEO-b-PSPA are crucial for polymersome stabilization in the zwitterionic MC state. Moreover, reversible phototriggered SP-to-MC polymersome transition is accompanied by membrane polarity and permeability switching from being nonimpermeable to selectively permeable toward noncharged, charged, and zwitterionic small molecule species below critical molar masses. Intriguingly, UV-actuated MC polymersomes possess two types of release modules: (1) sustained release upon short UV irradiation duration by taking advantage of the unexpectedly slow spontaneous MC-to-SP transition kinetics (t 1/2 > 20 h) under dark conditions; (2) on-demand and switchable release under alternated UV–vis light irradiation. We further demonstrate photoswitchable spatiotemporal release of 4′,6-diamidino-2-phenylindole (DAPI, cell nuclei-staining dye) within living HeLa cells.
We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal ...carcinoma (NPC).
We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set (
= 470), and then validated it on a test set (
= 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein-Barr virus (EBV) DNA.
A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS (
< 0.001). Using these signatures, we proposed a radiomics nomogram with a C-index of 0.754 95% confidence interval (95% CI), 0.709-0.800 in the training set and 0.722 (95% CI, 0.652-0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively (
< 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC.
Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.
•A data-driven lung nodule segmentation method without involving shape hypothesis.•Two-branch convolutional neural networks extract both 3D and multi-scale 2D features.•A novel central pooling layer ...is proposed for feature selection.•We propose a weighted sampling method to solve imbalanced training label problem.•The method shows strong performance for segmenting juxtapleural nodules.
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Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%.
Solution self-assembly of block copolymers (BCPs) typically generates spheres, rods, and vesicles. The reproducible bottom-up fabrication of stable planar nanostructures remains elusive due to their ...tendency to bend into closed bilayers. This morphological vacancy renders the study of shape effects on BCP nanocarrier-cell interactions incomplete. Furthermore, the fabrication of single BCP assemblies with built-in drug delivery functions and geometry-optimized performance remains a major challenge. We demonstrate that PEG-b-PCPTM polyprodrug amphiphiles, where PEG is poly(ethylene glycol) and PCPTM is polymerized block of reduction-cleavable camptothecin (CPT) prodrug monomer, with >50 wt % CPT loading content can self-assemble into four types of uniform nanostructures including spheres, large compound vesicles, smooth disks, and unprecedented staggered lamellae with spiked periphery. Staggered lamellae outperform the other three nanostructure types, exhibiting extended blood circulation duration, the fastest cellular uptake, and unique internalization pathways. We also explore shape-modulated CPT release kinetics, nanostructure degradation, and in vitro cytotoxicities. The controlled hierarchical organization of polyprodrug amphiphiles and shape-tunable biological performance opens up new horizons for exploring next-generation BCP-based drug delivery systems with improved efficacy.
Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the ...traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval CI: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine.
RSNA, 2016 Online supplemental material is available for this article.
We evaluated the performance of the newly proposed radiomics of multiparametric MRI (RMM), developed and validated based on a multicenter dataset adopting a radiomic strategy, for pretreatment ...prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer.
A total of 586 potentially eligible patients were retrospectively enrolled from four hospitals (primary cohort and external validation cohort 1-3). Quantitative imaging features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging before NAC for each patient. With features selected using a coarse to fine feature selection strategy, four radiomic signatures were constructed based on each of the three MRI sequences and their combination. RMM was developed based on the best radiomic signature incorporating with independent clinicopathologic risk factors. The performance of RMM was assessed with respect to its discrimination and clinical usefulness, and compared with that of clinical information-based prediction model.
Radiomic signature combining multiparametric MRI achieved an AUC of 0.79 (the highest among the four radiomic signatures). The signature further achieved good performances in hormone receptor-positive and HER2-negative group and triple-negative group. RMM yielded an AUC of 0.86, which was significantly higher than that of clinical model in two of the three external validation cohorts.
The study suggested a possibility that RMM provided a potential tool to develop a model for predicting pCR to NAC in breast cancer.
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