FORCE dual-energy CT in pathological grading of clear cell renal cell carcinoma

Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma

OBJECTIVES

We evaluated the value of dual-energy computed tomography (DECT) parameters derived from pancreatic ductal adenocarcinoma (PDAC) to discriminate between high- and low-grade tumors and predict overall survival (OS) in patients.

METHODS

Data were retrospectively collected from 169 consecutive patients with pathologically confirmed PDAC who underwent third-generation dual-source DECT enhanced dual-phase scanning before surgery between January 2017 and March 2023. Patients with prior treatments, other malignancies, small tumors, or poor-quality scans were excluded. Two radiologists evaluated three clinical and seven radiological features and measured sixteen DECT-derived parameters. Univariate and multivariate analyses were applied to select independent predictors. A prediction model and a corresponding nomogram were developed, and the area under the curve (AUC), calibration, and clinical applicability were assessed. The correlations between factors and OS were evaluated using Kaplan-Meier survival and Cox regression analyses.

RESULTS

One hundred sixty-nine patients were randomly divided into training (n = 118) and validation (n = 51) cohorts, among which 43 (36.4%) and 19 (37.3%) had high-grade PDAC confirmed by pathology, respectively. The vascular invasion, normalized iodine concentration in the venous phase, and effective atomic number in the venous phase were independent predictors for histological grading. A nomogram was constructed to predict the risk of high-grade tumors in PDAC, with AUCs of 0.887 and 0.844 in the training and validation cohorts, respectively. The nomogram exhibited good calibration and was more beneficial than a single parameter in both cohorts. Pathological- and nomoscore-predicted high-grade PDACs were associated with poor OS (all p < 0.05).

CONCLUSIONS

The nomogram, which combines DECT parameters and radiological features, can predict the histological grade and OS in patients with PDAC before surgery.

KEY POINTS

Question Preoperative determination of histological grade in PDAC is crucial for guiding treatment, yet current methods are invasive and limited. Findings A DECT-based nomogram combining vascular invasion, normalized iodine concentration, and effective atomic number accurately predicts histological grade and OS in PDAC patients. Clinical relevance The DECT-based nomogram is a reliable, non-invasive tool for predicting histological grade and OS in PDAC. It provides essential information to guide personalized treatment strategies, potentially improving patient management and outcomes.

Predicting post-surgical complications using renal scoring systems

Current surgical approaches for renal malignancies primarily rely on qualitative factors such as patient preferences, surgeon experience, and hospital capabilities. Applying a quantitative method for consistent and reliable assessment of renal lesions would significantly enhance surgical decision-making and facilitate data comparison. Nephrometry scoring (NS) systems systematically evaluate and describe renal tumors based on their anatomical features. These scoring systems, including R.E.N.A.L., PADUA, MAP scores, C-index, CSA, and T-index, aim to predict surgical complications by evaluating anatomical and patient-specific factors. In this review paper, we explore the components and methodologies of these scoring systems, compare their effectiveness and limitations, and discuss their application in advancing patient care and optimizing surgical outcomes.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) patients with a high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC. This study aimed to assess whether quantitative parameters derived from pretreatment dual-energy computed tomography (DECT) can predict the histopathological features of patients with HNSCC.

METHODS

In this study, 72 consecutive patients with pathologically confirmed HNSCC were enrolled and underwent dual-phase (noncontrast-enhanced phase and contrast-enhanced phase) DECT examinations. Normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λ_HU), and normalized effective atomic number (NZ_eff) were calculated. The attenuation values on 40-140 keV noise-optimized virtual monoenergetic images [VMIs (+)] in the contrast-enhanced phase were recorded. The diagnostic performance of the quantitative parameters for predicting histopathological features, including tumor grade, LVI, and PNI, was assessed by receiver operating characteristic curves.

RESULTS

The NIC, λ_HU, NZ_eff, and attenuation value on the VMIs (+) at 40 keV (A₄₀) in the grade III group, LVI-positive group, and PNI-positive group were significantly higher than those in the grade I and II groups, the LVI-negative group, and the PNI-negative group (all P values <0.05). A multivariate logistic regression model combining these 4 quantitative parameters improved the diagnostic performance of the model in predicting tumor grade, LVI, and PNI (areas under the curve: 0.969, 0.944, and 0.931, respectively).

CONCLUSIONS

Quantitative parameters derived from pretreatment DECT, including NIC, λ_HU, NZ_eff, and A_4,0 were found to be imaging markers for predicting the histopathological characteristics of HNSCC. Combining all these characteristics improved the predictive performance of the model.

Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma

Background

To examine the value of energetic-spectrum computed tomography (spectral CT) quantitative parameters in renal cell carcinoma (RCC) microvascular angiogenesis.

Methods

The authors evaluated 32 patients with pathologically confirmed RCC who underwent triple-phase contrast-enhanced CT with spectral CT imaging mode from January 2017 to December 2019. Quantitative parameters include parameters derived from iodine concentration (IC) and water concentration (WC) of 120 keV monochromatic images. All specimens were evaluated including the microvascular density (MVD), microvascular area (MVA) and so on. The correlation between IC and WC (including average values and random values) with microvascular parameters were analyzed with Pearson or Spearman rank correlation coefficients.

Results

The MVD of all tumors was 26.00 (15.00–43.75) vessels per field at × 400 magnification. The MVD of RCC correlated positively with the mean IC, mean WC, mean NWC, mean NIC, random IC, random NIC in renal cortical phase, WCD₁, WCD₂, NWCD₂ and ICD₁ (Spearman rank correlation coefficients, r range, 0.362–0.533; all p < 0.05). The MVA of all tumors was (16.16 ± 8.98) % per field at × 400 magnification. The MVA of RCC correlated positively with the mean IC, mean WC, mean NWC, mean NIC, random IC, random NIC in renal cortical, mean WC and mean NWC in renal parenchymal phase, WCD₁, WCD₂, WCD₃, NWCD₂, and NWCD₃ (Pearson or Spearman rank correlation coefficients, r range, 0.357–0.576; all p < 0.05). Microvascular grading correlated positively with the mean NWC, mean NIC and random NIC in renal cortical phase, mean NWC in renal parenchymal phase, NWCD₂, WCD₃, NWCD₃, NICD₂ and NICD₃ (Spearman rank correlation coefficients, r range, 0.367–0.520; all p < 0.05). As for tumor diameter (55.19 ± 19.15), μm, only NWCD₃ was associated with it (Spearman rank correlation coefficients, r = 0.388; p < 0.05).

Conclusions

ICD and WCD of spectral CT have a potential for evaluating RCC microvascular angiogenesis. MVD, MVA and microvascular grade showed moderate positive correlation with ICD and WCD. ICD displayed more relevant than that of WCD. The parameters of renal cortical phase were the best in three phases. NICD and NWCD manifested stronger correlation with microvascular parameters than that of ICD and WCD.