Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma

Radiomics reproducibility in computed tomography through changes of ROI size, resolution, and hounsfield unit: A phantom study

INTRODUCTION

Although radiomics has revealed an intriguing perspective for quantitative radiology, the impact of scanning parameters on its outcomes must be considered. In this study, the effects of changes in the region of interest (ROI) sizes, Hounsfield Unit (HU), and resolution of computed tomography (CT) on feature reproducibility have been investigated.

METHODS

The GAMMEX 464 phantom was used to evaluate the reproducibility of radiomics features across different ROI sizes, HU, and resolution. Data were acquired using a consistent system setup, with the phantom repositioned for each scan. The first acquisition series examined the effects of different ROI sizes and resolutions (1, 3, and 5 mm) on feature reproducibility. The second series assessed the impact of different HU and resolution. Segmentation and feature extraction were performed using LIFEx 7.1.0 software, focusing on textural radiomics features. Statistical analysis involved calculating the coefficient of variation (COV) to categorize feature variability. COV <5 % was considered highly stable.

RESULTS

Out of the 32 textural features studied, the analysis of changes in ROI size with a resolution of 1 mm, 3 mm, and 5 mm revealed that 16, 17, and 18 features had high reproducibility, with a COV<5 %. Polyethylene, acrylic, and water also demonstrated stable textural features across changes in scan parameters and image resolutions, with 4 reproducible features in all resolutions. The grey-level run length matrix (GLRLM) and grey-level zone length matrix (GLZLM) radiomics groups were highly stable in the context of variations in scan parameters and different materials.

CONCLUSION

The results of this study highlight the importance of standardizing radiomics studies to reduce the influence of pre-analysis steps on feature values. This standardization is crucial for guaranteeing the consistency of radiomics features under various imaging conditions. Additional research is required to enhance these results.

IMPLICATIONS FOR PRACTICE

To ensure the reproducibility and reliability of radiomics features, it is imperative to standardize scanning parameters and pre-analysis protocols. This standardization will enhance the consistency of radiomics applications in both clinical and research environments.

Rate of benign histology after resection of suspected renal cell carcinoma: multicenter comparison between Korea and the United States

BACKGROUND

In the United States, the rate of benign histology among resected renal tumors suspected to be malignant is increasing. We evaluated the rates in the Republic of Korea and assessed the racial effect using recent multi-institutional Korean-United States data.

METHODS

We conducted a multi-institutional retrospective study of 11,529 patients (8,812 from The Republic of Korea and 2,717 from the United States) and compared the rates of benign histology between the two countries. To evaluate the racial effect, we divided the patients into Korean, Asian in the US, and Non-Asian in the US.

RESULTS

The rates of benign histology and small renal masses in Korean patients were significantly lower than that in United States patients (6.3% vs. 14.3%, p < 0.001) and (≤ 4 cm, 7.6% vs. 19.5%, p < 0.001), respectively. Women, incidentaloma, partial nephrectomy, minimally invasive surgery, and recent surgery were associated with a higher rate of benign histology than others.

CONCLUSIONS

In Korea, the rate of benign histology among resected renal tumors was significantly lower than that in the United States. This disparity could be caused by environmental or cultural differences rather than racial differences. Our findings suggest that re-evaluating current context-specific standards of care is necessary to avoid overtreatment.

CT-derived textural analysis parameters discriminate high-attenuation renal cysts from solid renal neoplasms

AIM

To assess the utility of textural features on computed tomography (CT) to differentiate high-attenuation cysts from solid renal neoplasms among indeterminate renal lesions detected incidentally on CT.

MATERIALS AND METHODS

Patients were included if they had an indeterminate renal lesion on CT that was subsequently characterised on ultrasound or magnetic resonance imaging (MRI). Up to three lesions per patient were included if they had a size ≥10 mm and density of 20-70 HU on unenhanced CT or any single phase of contrast-enhanced CT. Cases were categorised as benign or most likely benign cysts (Bosniak II and IIF) versus indeterminate (Bosniak III), mixed solid and cystic (Bosniak IV), or solid renal lesions. A random forest model was generated using 95 textural parameters and four clinical parameters for each lesion.

RESULTS

Two hundred and thirty-four patients were included who had a total of 278 lesions. Of these, 193 (69%) were benign or most likely benign cysts and 85 (31%) were indeterminate, mixed cystic and solid, or solid renal lesions. The random forest model had an area under the curve of 0.71 (95% confidence interval [CI]: 0.65, 0.78), with a sensitivity and specificity of 81.2% and 38.9%, respectively.

CONCLUSION

A multivariate model including textural and clinical parameters had moderate overall performance for discriminating benign or likely benign cysts from indeterminate, mixed solid and cystic, or solid renal lesions. This study serves as a proof of concept and may reduce the need for further follow-up by characterising a significant portion of indeterminate lesions on CT as benign.

Textural Analysis as a Predictive Biomarker in Rectal Cancer

Colorectal cancer (CRC) is a common deadly cancer. Early detection and accurate staging of CRC enhance good prognosis and better treatment outcomes. Rectal cancer staging is the cornerstone for selecting the best treatment approach. The standard gold method for rectal cancer staging is pelvic MRI. After staging, combining surgery and chemoradiation is the standard management aiming for a curative outcome. Textural analysis (TA) is a radiomic process that quantifies lesions' heterogenicity by measuring pixel distribution in digital imaging. MRI textural analysis (MRTA) of rectal cancer images is growing in current literature as a future predictor of outcomes of rectal cancer management, such as pathological response to neoadjuvant chemoradiotherapy (NCRT), survival, and tumour recurrence. MRTA techniques could validate alternative approaches in rectal cancer treatment, such as the wait-and-watch (W&W) approach in pathologically complete responders (pCR) following NCRT. We consider this a significant step towards implementing precision management in rectal cancer. In this narrative review, we summarize the current knowledge regarding the potential role of TA in rectal cancer management in predicting the prognosis and clinical outcomes, as well as aim to delineate the challenges which obstruct the implementing of this new modality in clinical practice.

The Potential of Visceral Adipose Tissue in Distinguishing Clear Cell Renal Cell Carcinoma from Renal Angiomyolipoma with Minimal Fat

Purpose

To overcome the challenge of preoperative differentiation between clear cell renal cell carcinoma (ccRCC) and renal angiomyolipoma with minimal fat (RMFAML), we evaluated the potential of visceral adipose tissue (VAT) in distinguishing RMFAML from ccRCC.

Patients and Methods

Patients (191) were divided into ccRCC and RMFAML groups according to postoperative pathology. Umbilical horizontal computed tomography (CT) images were used for visceral fat area (VFA), subcutaneous fat area (SFA) and total fat area (TFA) measurements. Logistic regression was used to identify risk factors for ccRCC. Areas under the receiver operating characteristic (ROC) curve (AUCs) were compared to identify the most valuable indicator for identifying ccRCC and RMFAML.

Results

In total, 166 patients had ccRCC, and 25 had RMFAML. ccRCC and RMFAML patients showed significant differences in age (P<0.001), sex (P<0.001), hypertension (P=0.027), BMI (P<0.001), SFA (P=0.046), VFA (P<0.001) and TFA (P<0.001). According to multiple logistic regression analysis, male sex [4.311 (1.469~12.653), p=0.008]; older age [1.047 (1.008~1.088), p=0.017]; and higher BMI [1.305 (1.088~1.566), p=0.004], SFA [1.013 (1.003~1.023), p=0.008], VFA [1.026 (1.012~1.041), p<0.001] and TFA [1.011 (1.005~1.017), p=0.001] were associated with ccRCC. The AUCs of sex (male), age, BMI, TFA, VFA, and SFA were 0.726, 0.687, 0.783, 0.769, 0.840, and 0.645, respectively. The VFA cut-off value was 69.99 cm². The sensitivity and specificity of higher VFA (≥69.99 cm²) for ccRCC diagnosis were 79.52% and 80.00%, respectively.

Conclusion

In differentiating ccRCC from RMFAML, male sex, older age, and higher BMI, TFA, SFA, and VFA are risk factors for ccRCC. VFA is the most effective indicator for identifying ccRCC.

Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study

We aimed to evaluate radiomic features' stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom's center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann-Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.