Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection

Study of prediction model for high-grade meningioma using fractal geometry combined with radiological features

PURPOSE

To establish a prediction model combining fractal geometry and radiological features, which consider the complexity of tumour morphology advancing beyond the limitations of previous models.

METHODS

A total of 227 patients at the First Affiliated Hospital of Harbin Medical University from July 2021 to November 2023 were included. Fractal geometry was calculated and the radiomics features were extracted from regions of interest (ROIs). Weighted Gene Co-Expression Network Analysis (WGCNA) was employed for preliminary screening to identify those that were significantly associated with high-grade meningioma. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression was employed for further screening the radiomics features. Area under curve (AUC) was to evaluate models' performance.

RESULTS

In entire patient cohort, low-grade meningiomas had significantly lower fractal dimensions (P = 0.01), while high-grade meningiomas had higher lacunarity (P = 0.049). Fractal dimension (OR 6.8, 95% CI 1.49-36.51, P = 0.017), lacunarity (OR 3.7, 95% CI 1.36-11.75, P = 0.014), and Rscore (OR 2.8, 95% CI 1.55-5.75, P = 0.002) were independent risk factors for high-grade meningiomas. The final results demonstrated that the "fractal geometry + radiological features (semantic features + radiomics features)" model exhibited the most optimal performance in predicting high-grade meningioma, with an AUC of 0.854 in the training cohort and 0.757 in the validation cohort.

CONCLUSION

Significant differences in fractal dimension and lacunarity exist between high-grade and low-grade meningiomas, which can be potential predictive factors. The developed predictive model demonstrated good performance in predicting high-grade meningiomas.

The Value of Biological and Conditional Factors for Staging of Patients with Resectable Pancreatic Cancer Undergoing Upfront Resection: A Nationwide Analysis

BACKGROUND

Novel definitions suggest that resectability status for pancreatic ductal adenocarcinoma (PDAC) should be assessed beyond anatomical criteria, considering both biological and conditional factors. This has, however, yet to be validated on a nationwide scale. This study evaluated the prognostic value of biological and conditional factors for staging of patients with resectable PDAC.

PATIENTS AND METHODS

A nationwide observational cohort study was performed, including all consecutive patients who underwent upfront resection of National Comprehensive Cancer Network resectable PDAC in the Netherlands (2014-2019) with complete information on preoperative carbohydrate antigen (CA) 19-9 and Eastern Cooperative Oncology Group (ECOG) performance status. PDAC was considered biologically unfavorable (RB+) if CA19-9 ≥ 500 U/mL and favorable (RB-) otherwise. ECOG ≥ 2 was considered conditionally unfavorable (RC+) and favorable otherwise (RC-). Overall survival (OS) was assessed using Kaplan-Meier and Cox-proportional hazard analysis, presented as hazard ratios (HRs) with 95% confidence interval (CI).

RESULTS

Overall, 688 patients were analyzed with a median overall survival (OS) of 20 months (95% CI 19-23). OS was 14 months (95% CI 10 months-median not reached) in 20 RB+C+ patients (3%; HR 1.61, 95% CI 0.86-2.70), 13 months (95% CI 11-15) in 156 RB+C- patients (23%; HR 1.86, 95% CI 1.50-2.31), and 21 months (95% CI 12-41) in 47 RB-C+ patients (7%; HR 1.14, 95% CI 0.80-1.62) compared with 24 months (95% CI 22-27) in 465 patients with RB-C- PDAC (68%; reference).

CONCLUSIONS

Survival after upfront resection of anatomically resectable PDAC is worse in patients with CA19-9 ≥ 500 U/mL, while performance status had no impact. This supports consideration of CA19-9 in preoperative staging of resectable PDAC.

A novel body composition risk score (B-Score) and overall survival among patients with nonmetastatic breast cancer

BACKGROUND & AIMS

Measurements (amount, distribution, and radiodensity) of muscle and adipose tissue were reported to be individually associated with overall survival in patients with breast cancer. However, they were not typically combined to develop an overall risk score, which can identify patients at high risk of death and prioritize patients in need of dietary and lifestyle interventions. Thus, we aimed to develop a novel composite body composition risk score (B-Score).

METHODS

We included 3105 patients with stage II or III breast cancer at Kaiser Permanente Northern California and Dana Farber Cancer Institute. From CT scans at diagnosis, we assessed areas and radiodensity of muscle and adipose tissue at the third lumber vertebrae. We considered skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and SAT radiodensity as they were independent prognostic factors for overall survival. Each measurement was dichotomized using optimal stratification, with low SMI (<40.1 cm2/m2), high SATI (≥75.7 cm2/m2), and high SAT radiodensity (≥-97.2HU) considered risk factors. We calculated B-Score as the sum of these factors and estimated its association with overall survival using Cox proportional hazards regression with adjustment for clinicopathologic factors.

RESULTS

Mean (standard deviation) age was 53.9 (11.8) years, 70.3% were Non-Hispanic White, and 60.5% were stage II. Most patients (60.6%) had only one body composition risk factor (B-Score = 1). Compared to those with no risk factors (B-Score = 0), the risk of death increased with more body composition risk factors: the adjusted hazard ratios were 1.10 (95% CI: 0.85, 1.42), 1.47 (95% CI: 1.12, 1.92), and 2.11 (95% CI: 1.26, 3.53) for B-Scores of 1, 2, and 3, respectively (Ptrend < 0.001).

CONCLUSIONS

More unfavorable body composition characteristics were associated with increased risks of overall mortality in a dose-response manner. Considering body composition measurements together as a composite score (B-Score) may improve risk stratification and inform dietary and lifestyle interventions following breast cancer diagnosis.

Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN)

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.

Fully automated 3D body composition analysis and its association with overall survival in head and neck squamous cell carcinoma patients

Objectives

We developed a method for a fully automated deep-learning segmentation of tissues to investigate if 3D body composition measurements are significant for survival of Head and Neck Squamous Cell Carcinoma (HNSCC) patients.

Methods

3D segmentation of tissues including spine, spine muscles, abdominal muscles, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and internal organs within volumetric region limited by L1 and L5 levels was accomplished using deep convolutional segmentation architecture - U-net implemented in a nnUnet framework. It was trained on separate dataset of 560 single-channel CT slices and used for 3D segmentation of pre-radiotherapy (Pre-RT) and post-radiotherapy (Post-RT) whole body PET/CT or abdominal CT scans of 215 HNSCC patients. Percentages of tissues were used for overall survival analysis using Cox proportional hazard (PH) model.

Results

Our deep learning model successfully segmented all mentioned tissues with Dice's coefficient exceeding 0.95. The 3D measurements including difference between Pre-RT and post-RT abdomen and spine muscles percentage, difference between Pre-RT and post-RT VAT percentage and sum of Pre-RT abdomen and spine muscles percentage together with BMI and Cancer Site were selected and significant at the level of 5% for the overall survival. Aside from Cancer Site, the lowest hazard ratio (HR) value (HR, 0.7527; 95% CI, 0.6487-0.8735; p = 0.000183) was observed for the difference between Pre-RT and post-RT abdomen and spine muscles percentage.

Conclusion

Fully automated 3D quantitative measurements of body composition are significant for overall survival in Head and Neck Squamous Cell Carcinoma patients.