Novel Volumetric and Morphological Parameters Derived from Three-dimensional Virtual Modeling to Improve Comprehension of Tumor's Anatomy in Patients with Renal Cancer

Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer

PURPOSE

To determine whether the morphological parameters of prostate zones and tumors on magnetic resonance imaging (MRI) can predict the tumor-stage (T-stage) of prostate cancer (PCa) and establish an optimal T-stage diagnosis protocol based on three-dimensional reconstruction and quantization after image segmentation.

METHODS

A dataset of the prostate MRI scans and clinical data of 175 patients who underwent biopsy and had pathologically proven PCa from January 2018 to November 2020 was retrospectively analyzed. The authors manually segmented and measured the volume, major axis, and cross-sectional area of the peripheral zone (PZ), transition zone, central zone (CZ), anterior fibromuscular stroma, and tumor. The differences were evaluated by the One-Way analysis of variance, Pearson's chi-squared test, or independent samples t-test. Spearman's correlation coefficient and receiver operating characteristic curve analyses were also performed. The cut-off values of the T-stage diagnosis were generated using Youden's J index.

RESULTS

The prostate volume (PV), PZ volume (PZV), CZ volume, tumor's major axis (TA), tumor volume (TV), and volume ratio of the TV and PV were significantly different among stages T1 to T4. The cut-off values of the PV, PZV, CZV, TA, TV, and the ratio of TV/PV for the discrimination of the T1 and T2 stages were 53.63 cm3, 11.60 cm3, 1.97 cm3, 2.30 mm, 0.90 cm3, and 0.03 [area under the curves (AUCs): 0.628, 0.658, 0.610, 0.689, 0.724, and 0.764], respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T2 and T3 stages were 2.80 mm, 8.29 cm3, and 0.12 (AUCs: 0.769, 0.702, and 0.688), respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T3 and T4 stages were 4.17 mm, 18.71 cm3, and 0.22 (AUCs: 0.674, 0.709, and 0.729), respectively.

CONCLUSION

The morphological parameters of the prostate zones and tumors on the MRIs are simple and valuable diagnostic factors for predicting the T-stage of patients with PCa, which can help make accurate diagnoses and lateral treatment decisions.

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer

Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.

A Novel Three-dimensional Planning Tool for Selective Clamping During Partial Nephrectomy: Validation of a Perfusion Zone Algorithm

BACKGROUND

Selective clamping during robot-assisted partial nephrectomy (RAPN) requires extensive knowledge on patient-specific renal vasculature, obtained through imaging.

OBJECTIVE

To validate an in-house developed perfusion zone algorithm that provides patient-specific three-dimensional (3D) renal perfusion information.

DESIGN, SETTING, AND PARTICIPANTS

Between October 2020 and June 2022, 25 patients undergoing RAPN at Ghent University Hospital were included. Three-dimensional models, based on preoperative computed tomography (CT) scans, showed the clamped artery's ischemic zone, as calculated by the algorithm.

SURGICAL PROCEDURE

All patients underwent selective clamping during RAPN. Indocyanine green (ICG) was administered to visualize the true ischemic zone perioperatively. Surgery was recorded for a postoperative analysis.

MEASUREMENTS

The true ischemic zone of the clamped artery was compared with the ischemic zone predicted by the algorithm through two metrics: (1) total ischemic zone overlap and (2) tumor ischemic zone overlap. Six urologists assessed metric 1; metric 2 was assessed objectively by the authors.

RESULTS AND LIMITATIONS

In 92% of the cases, the algorithm was sufficiently accurate to plan a selective clamping strategy. Metric 1 showed an average score of 4.28 out of 5. Metric 2 showed an average score of 4.14 out of 5. A first limitation is that ICG can be evaluated only at the kidney surface. A second limitation is that mainly patients with impaired renal function are expected to benefit from this technology, but contrast-enhanced CT is required at present.

CONCLUSIONS

The proposed new tool demonstrated high accuracy when planning selective clamping for RAPN. A follow-up prospective study is needed to determine the tool's clinical added value.

PATIENT SUMMARY

In partial nephrectomy, the surgeon has no information on which specific arterial branches perfuse the kidney tumor. We developed a surgeon support system that visualizes the perfusion zones of all arteries on a three-dimensional model and indicates the correct arteries to clamp. In this study, we validate this tool.

3D renal model for surgical planning of partial nephrectomy: A way to improve surgical outcomes

OBJECTIVE

to evaluate the impact of 3D model for a comprehensive assessment of surgical planning and quality of partial nephrectomy (PN).

MATERIALS AND METHODS

195 patients with cT1-T2 renal mass scheduled for PN were enrolled in two groups: Study Group (n= 100), including patients referred to PN with revision of both 2D computed tomography (CT) imaging and 3D model; Control group (n= 95), including patients referred to PN with revision of 2D CT imaging. Overall, 20 individuals were switched to radical nephrectomy (RN). The primary outcome was the impact of 3D models-based surgical planning on Trifecta achievement (defined as the contemporary absence of positive surgical margin, major complications and ≤30% postoperative eGFR reduction). The secondary outcome was the impact of 3D models on surgical planning of PN. Multivariate logistic regressions were used to identify predictors of selective clamping and Trifecta's achievement in patients treated with PN (n=175).

RESULTS

Overall, 73 (80.2%) patients in Study group and 53 (63.1%) patients in Control group achieved the Trifecta (p=0.01). The preoperative plan of arterial clamping was recorded as clampless, main artery and selective in 22 (24.2%), 22 (24.2%) and 47 (51.6%) cases in Study group vs. 31 (36.9%), 46 (54.8%) and 7 (8.3%) cases in Control group, respectively (p<0.001). At multivariate logistic regressions, the use of 3D model was found to be independent predictor of both selective or super-selective clamping and Trifecta's achievement.

CONCLUSION

3D-guided approach to PN increase the adoption of selective clamping and better predict the achievement of Trifecta.

Percutaneous Microcoil Localization of a Small, Totally Endophytic Renal Mass for Nephron-Sparing Surgery: A Case Report and Literature Review

Small, totally endophytic renal masses present a technical challenge for surgical extirpation due to poor identifiability during surgery. The method for the precise localization of totally endophytic tumours before nephron-sparing surgery could be optimized. An asymptomatic 70-year-old male presented with a right-sided, 16-mm, totally endophytic renal mass on computed tomography (CT). CT-guided percutaneous microcoil localization was carried out prior to laparoscopy to provide a direction for partial nephrectomy. During the 25 minutes of the localization procedure, the patient underwent five local CT scans, and his cumulative effective radiation dosage was 5.1 mSv. The span between localization and the start of the operation was 15 hours. The laparoscopic operation time was 105 minutes, and the ischaemia time was 25 minutes. The postoperative recovery was smooth, and no perioperative complications occurred. Pathology showed the mass to be renal clear cell carcinoma, WHO/ISUP grade 2, with a 2-mm, clear surgical margin. The patient remained free of recurrence on follow-up for eleven months. To our knowledge, this application of microcoil implantation prior to laparoscopic partial nephrectomy towards an intrarenal mass could be an early reported attempt for the localized method applied in renal surgery. The percutaneous microcoil localization of endophytic renal tumours is potentially safe and effective prior to laparoscopic partial nephrectomy.

New imaging technologies for robotic kidney cancer surgery

Objective

Kidney cancers account for approximately 2% of all newly diagnosed cancer in 2020. Among the primary treatment options for kidney cancer, urologist may choose between radical or partial nephrectomy, or ablative therapies. Nowadays, robotic-assisted partial nephrectomy (RAPN) for the management of renal cancers has gained popularity, up to being considered the gold standard. However, RAPN is a challenging procedure with a steep learning curve.

Methods

In this narrative review, different imaging technologies used to guide and aid RAPN are discussed.

Results

Three-dimensional visualization technology has been extensively discussed in RAPN, showing its value in enhancing robotic-surgery training, patient counseling, surgical planning, and intraoperative guidance. Intraoperative imaging technologies such as intracorporeal ultrasound, near-infrared fluorescent imaging, and intraoperative pathological examination can also be used to improve the outcomes following RAPN. Finally, artificial intelligence may play a role in the field of RAPN soon.

Conclusion

RAPN is a complex surgery; however, many imaging technologies may play an important role in facilitating it.

3D Virtual Modeling for Morphological Characterization of Pituitary Tumors: Preliminary Results on Its Predictive Role in Tumor Resection Rate

Among potential factors affecting the surgical resection in pituitary tumors, the role of tumor three-dimensional (3D) features is still unexplored. The aim of this study is to introduce the use of 3D virtual modeling for geometrical and morphological characterization of pituitary tumors and to evaluate its role as a predictor of total tumor removal. A total of 75 patients operated for a pituitary tumor have been retrospectively reviewed. Starting from patient imaging, a 3D tumor model was reconstructed, and 3D characterization based on tumor volume (Vol), area, sphericity (Spher), and convexity (Conv) was provided. The extent of tumor removal was then evaluated at post-operative imaging. Mean values were obtained for Vol (9117 ± 8423 mm3), area (2352 ± 1571 mm2), Spher (0.86 ± 0.08), and Conv (0.88 ± 0.08). Total tumor removal was achieved in 57 (75%) cases. The standard prognostic Knosp grade, Vol, and Conv were found to be independent factors, significantly predicting the extent of tumor removal. Total tumor resection correlated with lower Knosp grades (p = 0.032) and smaller Vol (p = 0.015). Conversely, tumors with a more irregular shape (low Conv) have an increased chance of incomplete tumor removal (p = 0.022). 3D geometrical and morphological features represent significant independent prognostic factors for pituitary tumor resection, and they should be considered in pre-operative planning to allow a more accurate decision-making process.