Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection.

Radiomics offers a noninvasive avenue for predicting clinicopathological factors. However, thorough investigations into a robust breast cancer outcome-predicting model and its biological significance remain limited. This study develops a robust radiomic model for prognosis prediction, and further excavates its biological foundation and transferring prediction performance. We retrospectively collected preoperative dynamic contrast-enhanced MRI data from three distinct breast cancer patient cohorts. In FUSCC cohort (n = 466), Lasso was used to select features correlated with patient prognosis and multivariate Cox regression was utilized to integrate these features and build the radiomic risk model, while multiomic analysis was conducted to investigate the model's biological implications. DUKE cohort (n = 619) and I-SPY1 cohort (n = 128) were used to test the performance of the radiomic signature in outcome prediction. A thirteen-feature radiomic signature was identified in the FUSCC cohort training set and validated in the FUSCC cohort testing set, DUKE cohort and I-SPY1 cohort for predicting relapse-free survival (RFS) and overall survival (OS) (RFS: p = 0.013, p = 0.024 and p = 0.035; OS: p = 0.036, p = 0.005 and p = 0.027 in the three cohorts). Multiomic analysis uncovered metabolic dysregulation underlying the radiomic signature (ATP metabolic process: NES = 1.84, p-adjust = 0.02; cholesterol biosynthesis: NES = 1.79, p-adjust = 0.01). Regarding the therapeutic implications, the radiomic signature exhibited value when combining clinical factors for predicting the pathological complete response to neoadjuvant chemotherapy (DUKE cohort, AUC = 0.72; I-SPY1 cohort, AUC = 0.73). In conclusion, our study identified a breast cancer outcome-predicting radiomic signature in a multicenter radio-multiomic study, along with its correlations with multiomic features in prognostic risk assessment, laying the groundwork for future prospective clinical trials in personalized risk stratification and precision therapy.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets.

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.

Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods.

OBJECTIVE: Automatic tumour segmentation and volumetry is useful in cancer staging and treatment outcome assessment. This paper presents a performance benchmarking study on liver tumour segmentation for three semiautomatic algorithms: 2D region growing with knowledge-based constraints (A1), 2D voxel classification with propagational learning (A2) and Bayesian rule-based 3D region growing (A3). METHODS: CT data from 30 patients were studied, and 47 liver tumours were isolated and manually segmented by experts to obtain the reference standard. Four datasets with ten tumours were used for algorithm training and the remaining 37 tumours for testing. Three evaluation metrics, relative absolute volume difference (RAVD), volumetric overlap error (VOE) and average symmetric surface distance (ASSD), were computed based on computerised and reference segmentations. RESULTS: A1, A2 and A3 obtained mean/median RAVD scores of 17.93/10.53%, 17.92/9.61% and 34.74/28.75%, mean/median VOEs of 30.47/26.79%, 25.70/22.64% and 39.95/38.54%, and mean/median ASSDs of 2.05/1.41 mm, 1.57/1.15 mm and 4.12/3.41 mm, respectively. For each metric, we obtained significantly lower values of A1 and A2 than A3 (P < 0.01), suggesting that A1 and A2 outperformed A3. CONCLUSIONS: Compared with the reference standard, the overall performance of A1 and A2 is promising. Further development and validation is necessary before reliable tumour segmentation and volumetry can be widely used clinically.

Involvement of growth factors in diabetes mellitus and its complications: A general review.

Diabetes mellitus (DM) is a major endocrine metabolic disease and is marked by a lack of insulin. The complication of DM is one of the most difficult problems in medicine. The initial translational studies revealed that growth factors have a major role in integrating tissue physiology and in embryology as well as in growth, maturation and tissue repair. In some tissues affected by diabetes, growth factors are induced by a relative deficit or excess. Fibroblast growth factor 21 (FGF21) is a promising regulator of glucose and lipid metabolism with multiple beneficial effects including hypoglycemic and lipid-lowering. Vascular endothelial growth factor (VEGF) is a potent angiogenic and vascular permeability factor and is implicated in both of these complications in diabetes. Increase or decrease in the production of transforming growth factor-ß1 (TGF-ß1) has been associated with diabetic nephropathy and retinopathy. The insulin-like growth factor-I (IGF-I) is a naturally-occurring single chain polypeptide which has been widely used in the treatment of diabetic glomerular and renal tubular injuries. This review summarizes the recent evidences for an involvement of growth factors in diabetic complications, focusing on their emergence in sequence of events leading to vascular complications or their potential therapeutic role in these diseases. Growth factor therapy in diabetic foot ulcers is already a clinical reality. As methods to finely regulate growth factors in a tissue and time-specific manner are further developed and tested, regulation of the growth factor to normal level in vivo may well become a therapy to prevent and treat diabetic complications.

Correlation between MR imaging-derived nasopharyngeal carcinoma tumor volume and TNM system.

PURPOSE: To measure nasopharyngeal carcinoma tumor volume based on magnetic resonance images using a validated semiautomated measurement methodology and correlate tumor volume with TNM T classification. METHODS AND MATERIALS: The study population consisted of 206 consecutive nasopharyngeal carcinoma patients who had magnetic resonance imaging staging scans. Tumor volume was measured using a semisupervised knowledge-based fuzzy clustering algorithm. Patients were divided into 4 groups according to TNM T classification. The difference in tumor volumes among the various TNM T-classification groups was examined. RESULTS: The mean tumor volume in each T-classification group is as follows: T1, 8.6 mL +/- 5.0 (standard deviation [SD]); T2, 18.1 mL +/- 8.1 (SD); T3, 25.8 mL +/- 14.1 (SD); and T4, 36.2 mL +/- 18.9 (SD). The mean tumor volume increased significantly with advancing T classification (p < 0.0001). Tumor volume in a more advanced T group was significantly larger than that in an adjacent early T group (p < 0.01). CONCLUSION: Validated magnetic resonance imaging-based tumor volume shows positive correlation between tumor volume and advancing T-classification groups. It may be possible to incorporate tumor volume as an additional prognostic factor into the existing TNM system.

Tongue carcinoma: tumor volume measurement.

PURPOSE: To validate the semiautomated methods of tongue carcinoma tumor volume measurement by comparing the conventional manual trace method with 2 semiautomated computer methods: seed growing and region deformation. MATERIALS AND METHODS: The study population consisted of 16 patients with histology-proven tongue carcinoma. Two head-and-neck radiologists independently measured the tumor volume demonstrated on pretreatment T2-weighted magnetic resonance data sets. The tumor volumes were measured using manual tracing and semiautomated seed growing and region deformation algorithm. Data were recorded for analysis of interoperator variance and interobserver reliability at volume and pixel levels. RESULTS: There was no significant difference between the manually traced volume and semiautomated segmentation volumes for both operators. No significant difference was found in interobserver variance among the 3 methods at volume level. However, there was significant difference between manual tracing and semiautomated segmentation methods in interobserver reliability at pixel level. CONCLUSION: The semiautomated methods could achieve satisfactory segmentation results. They could also reduce interoperator variance and obtain a higher interobserver reliability. This study validates the use of semiautomated volume measurement methods for tongue carcinoma.

Indication of lower neck irradiation in nasopharyngeal carcinoma without nodal metastasis: the potential impact of tumor volume.

BACKGROUND: Elective radiation of lower neck is controversial for nasopharyngeal carcinoma (NPC) without lymph node metastasis (N0 disease). Tumor volume is an important prognostic indicator. The objective of this study is to explore the potential impact of tumor volume on the indication of the lower neck irradiation for N0-NPC, by a qualitative evaluation of the relationship between tumor volume and nodal metastasis. METHODS: Magnetic resonance (MR) images of 99 consecutive patients with NPC who underwent treatment were retrospectively reviewed. Primary tumor volumes of NPC were semi-automatically measured, nodal metastases were N-classified and neck level involvements were examined. Distributions of tumor volumes among N-category-based groups and distributions of N-categories among tumor volume-based groups were analyzed, respectively. RESULTS: The numbers of patients with N0 to N3 disease were 12, 39, 32, and 16, respectively. The volumes of primary tumor were from 3.3 to 89.6 ml, with a median of 17.1 ml. For patients with nodal metastasis, tumor volume did not increase significantly with the advancing of N-category (P > 0.05). No significant difference was found for the distribution of N1, N2, and N3 categories among tumor volume-based groups (P > 0.05). Nevertheless patients with nodal metastasis had significantly larger tumor volumes than those without metastasis (P < 0.05). Patients with larger tumor volumes were associated with an increased incidence of nodal metastasis. CONCLUSIONS: Certain positive correlations existed between tumor volume and the presence of nodal metastasis. The tumor volume (>10 ml) is a potential indicator for the lower neck irradiation for N0-NPC.

Extraction of metastatic lymph nodes from MR images using two deformable model-based approaches.

We presented and evaluated two deformable model-based approaches, region plus contour deformation (RPCD), and level sets to extract metastatic cervical nodal lesions from pretreatment T2-weighted magnetic resonance images. The RPCD method first uses a region deformation to achieve a rough boundary of the target node from a manually drawn initial contour, based on signal statistics. After that, an active contour deformation is employed to drive the rough boundary to the real node-normal tissue interface. Differently, the level sets move a manually drawn initial contour toward the desired nodal boundary under the control of the evolvement speed function, which is influenced by image gradient force. The two methods were tested by extracting 33 metastatic cervical nodes from 18 nasopharyngeal carcinoma patients. Experiments on a basis of pixel matching to reference standard showed that RPCD and level sets achieved averaged percentage matching at 82-84% and 87-88%, respectively. In addition, both methods had significantly lower interoperator variances than the manual tracing method. It was suggested these two methods could be useful tools for the evaluation of metastatic nodal volume as an indicator of classification and treatment response, or be alternatives for the delineation of metastatic nodal lesions in radiation treatment planning.

The relationship between nasopharyngeal carcinoma tumor volume and TNM T-classification: a quantitative analysis.

Recent findings show that tumor volume is a significant prognostic factor for the treatment of nasopharyngeal carcinoma (NPC). The inclusion of tumor volume as an additional prognostic factor in the UICC TNM classification system was suggested; however, how tumor volume could possibly be incorporated is still unexplored. In this paper, we report a quantitative analysis on the relationship between NPC tumor volume and T-classification, using the data from 206 NPC patients. By T-classification and semi-automatic tumor volume measurement, the difference in tumor volumes among the various TNM T-classification groups was examined. In addition, a statistics-based analysis scheme, which used the T-classification as the "gold standard", was proposed to classify NPC tumors into volume-based groups to explore the possible links. The results show that NPC tumor volume has positive correlation with advancing T-classification groups and significant difference existed in the distribution of T-classification among various volume-based groups (P < 0.001). By the proposed statistical scheme, tumor volume could be included as an additional prognostic factor in the TNM framework, following validation studies.

Nasopharyngeal carcinoma tumor volume measurement..

Tumor volume was measured in 69 patients with nasopharyngeal carcinoma. On transverse nonenhanced T1-weighted and gadolinium-enhanced T1-weighted magnetic resonance (MR) images, segmentation was performed by means of seed growing and knowledge-based fuzzy clustering methods. Data were compared with those collected with the manual tracing method and analyzed for interoperator variance and interobserver reliability. There was no significant difference between the volumes determined with manual tracing or semiautomated segmentation (P >.05). On the volume level, Pearson correction coefficients were close for both the manual tracing and semiautomated methods. Significant differences in interoperator variance existed between the two methods on the pixel level (P <.05). Compared with manual tracing, the semiautomated method helped reduce interoperator variance and obtain higher interobserver reliability. Findings in the current study validate the use of semiautomated volume measurement methods for nasopharyngeal carcinoma.