Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset ( n = 1474) encompassing radiomic features extracted from dynamic contrast–enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Triple-negative breast cancer (TNBC) is a subset of breast cancer with an adverse prognosis and significant tumor heterogeneity. Here, we extract quantitative radiomic features from contrast-enhanced magnetic resonance images to construct a breast cancer radiomic dataset (n = 860) and a TNBC radiogenomic dataset (n = 202). We develop and validate radiomic signatures that can fairly differentiate TNBC from other breast cancer subtypes and distinguish molecular subtypes within TNBC. A radiomic feature that captures peritumoral heterogeneity is determined to be a prognostic factor for recurrence-free survival (p = 0.01) and overall survival (p = 0.004) in TNBC. Combined with the established matching TNBC transcriptomic and metabolomic data, we demonstrate that peritumoral heterogeneity is associated with immune suppression and upregulated fatty acid synthesis in tumor samples. Collectively, this multi-omic dataset serves as a useful public resource to promote precise subtyping of TNBC and helps to understand the biological significance of radiomics.

We elected to analyze the correlation between the pre-treatment apparent diffusion coefficient (ADC) and the clinical, histological, and immunohistochemical status of rectal cancers. Forty-nine rectal cancer patients who received surgical resection without neoadjuvant therapy were selected that underwent primary MRI and diffusion-weighted imaging (DWI). Tumor ADC values were determined and analyzed to identify any correlations between these values and pre-treatment CEA or CA19-9 levels, and/or the histological and immunohistochemical properties of the tumor. Inter-observer agreement of confidence levels from two separate observers was suitable for ADC measurement (k = 0.775). The pre-treatment ADC values of different T stage tumors were not equal (p = 0.003). The overall trend was that higher T stage values correlated with lower ADC values. ADC values were also significantly lower for the following conditions: tumors with the presence of extranodal tumor deposits (p = 0.006) and tumors with CA19-9 levels ≥ 35 g/ml (p = 0.006). There was a negative correlation between Ki-67 LI and the ADC value (r = −0.318, p = 0.026) and between the AgNOR count and the ADC value (r = −0.310, p = 0.030). Significant correlations were found between the pre-treatment ADC values and T stage, extranodal tumor deposits, CA19-9 levels, Ki-67 LI, and AgNOR counts in our study. Lower ADC values were associated with more aggressive tumor behavior. Therefore, the ADC value may represent a useful biomarker for assessing the biological features and possible relationship to the status of identified rectal cancers.

Purpose: To determine the ability of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) to predict pathological complete response (pCR) before preoperative chemoradiotherapy (CRT) in locally advanced rectal cancer.Materials and Methods: In a prospective clinical trial, 38 enrolled patients underwent pre‐ and post‐CRT DCE‐MRI at 3.0T. The tumor length and the following perfusion parameters (K^trans, k_ep, v_e) were measured for the tumor and compared between the pCR group and the non‐pCR group, as well as before and after CRT. For categorical variable comparison, the Kruskal‐Wallis test was used. P < 0.05 was considered significant.Results: No difference in tumor length was found between the pCR and non‐pCR group pre‐ and post‐CRT (P = 0.26 (0.15,0.45), 0.35 (0.21,0.52), respectively). Before CRT, the mean tumor K^trans in the pCR group was significantly higher than in the non‐pCR group (P = 0.01). A K^trans of 0.66 emerged as the best cutoff for distinguishing pCR from non‐pCR. Regarding k_ep and v_e, significant differences were also observed between the pCR and non‐pCR groups (P = 0.02, 0.01, respectively). The mean K^trans, k_ep, and v_e values post‐CRT were lower in the pCR group than in the non‐pCR group, although there was no significant difference (P = 0.10 (0.04,0.16), 0.11 (0.07,0.26), 0.10 (0.06,0.23), respectively).Conclusion: Before neoadjuvant chemoradiotherapy in rectal cancer, DCE‐MRI can distinguish between complete and incomplete response using a K^trans threshold of 0.66 with a sensitivity of 100%. J. Magn. Reson. Imaging 2015;42:673–680.

To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.