Proton MR spectroscopy shows improved performance to segregate high-grade astrocytoma subgroups when defined with the new 2021 World Health Organization classification of central nervous system tumors.

OBJECTIVES: The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors prioritizes isocitrate dehydrogenase (IDH) mutation to define tumor types in diffuse gliomas, in contrast to the 2016 classification, which prioritized histological features. Our objective was to investigate the influence of this change in the performance of proton MR spectroscopy (1H-MRS) in segregating high-grade diffuse astrocytoma subgroups. METHODS: Patients with CNS WHO grade 3 and 4 diffuse astrocytoma, known IDH mutation status, and available 1H-MRS were retrospectively retrieved and divided into 4 groups based on IDH mutation status and histological grade. Differences in 1H-MRS between groups were analyzed with the Kruskal-Wallis test. The points on the spectrum that showed the greatest differences were chosen to evaluate the performance of 1H-MRS in discriminating between grades 3 and 4 tumors (WHO 2016 defined), and between IDH-mutant and IDH-wildtype tumors (WHO 2021). ROC curves were constructed with these points, and AUC values were calculated and compared. RESULTS: The study included 223 patients with high-grade diffuse astrocytoma. Discrimination between IDH-mutant and IDH-wildtype tumors showed higher AUC values (highest AUC short TE, 0.943; long TE, 0.864) and more noticeable visual differences than the discrimination between grade 3 and 4 tumors (short TE, 0.885; long TE, 0.838). CONCLUSION: Our findings suggest that 1H-MRS is more applicable to classify high-grade astrocytomas defined with the 2021 criteria. Improved metabolomic robustness and more homogeneous groups yielded better tumor type discrimination by 1H-MRS with the new criteria. CLINICAL RELEVANCE STATEMENT: The 2021 World Health Organization classification of brain tumors empowers molecular criteria to improve tumor characterization. This derives in greater segregation of high-grade diffuse astrocytoma subgroups by MR spectroscopy and warrants further development of brain tumor classification tools with spectroscopy. KEY POINTS: • The new 2021 updated World Health Organization classification of central nervous system tumors maximizes the role of molecular diagnosis in the classification of brain tumors. • Proton MR spectroscopy performs better to segregate high-grade astrocytoma subgroups when defined with the new criteria. • The study provides additional evidence of improved metabolic characterization of brain tumor subgroups with the new criteria.

Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging.

OBJECTIVES: To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer. MATERIALS AND METHODS: A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI. CONCLUSION: The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery. CLINICAL RELEVANCE STATEMENT: Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer. KEY POINTS: Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

Role of magnetic resonance imaging in the prediction of histological grade in soft tissue sarcomas.

BACKGROUND: Soft tissue sarcomas are rare malignant tumors with significant heterogeneity. The importance of classifying histological grades is fundamental to defining the treatment approach. OBJECTIVE: To evaluate magnetic resonance imaging (MRI) in predicting the histological grade of soft tissue sarcomas. METHODS: A retrospective observational study included patients over 18 years undergoing MRI and primary tumor surgery at AC Camargo Cancer Center from January 2015 to June 2022. Two radiologists evaluated MRI criteria (size, margin definition, heterogeneity of the T2 signal, high-intensity peritumoral signal on T2, and postperitumoral contrast), and a grading prediction score was calculated. χ2 and logistic regression analyses were conducted. RESULTS: Sixty-eight patients were included (38 men; median: 48 years). Moreover, 52 high-grade and 16 low-grade tumors were observed. The MRI criteria associated with histological grade were peritumoral high-intensity T2-weighted signals (p < 0.001) and peritumoral postcontrast enhancement (p = 0.006). Logistic regression confirmed their significance (odds ratio [OR]: 11.8 and 8.8, respectively). Each score point increment doubled the chance of high-grade tumors (OR: 2.0; p = 0.014). CONCLUSION: MRI effectively predicts histological grades of soft tissue sarcomas. Peritumoral high-intensity T2-weighted signals and peritumoral postcontrast enhancement are valuable indicators of high-grade tumors. This highlights MRI's importance in treatment decision-making for sarcoma patients.

Clinical features and outcomes of young patients with low-grade non-rhabdomyosarcoma soft tissue sarcomas treated with a risk-based strategy: A report from Children's Oncology Group study ARST0332.

BACKGROUND: In retrospective analyses, the Pediatric Oncology Group [POG) and the Federation National des Centres de Lutte Contre le Cancer (FNCLCC) histologic grade predict outcome in pediatric non-rhabdomyosarcoma soft tissue sarcoma (NRSTS), but prospective data on grading, clinical features, and outcomes of low-grade NRSTS are limited. METHODS: We analyzed patients less than 30 years of age enrolled on Children's Oncology Group (COG) study ARST0332 (NCT00346164) with POG grade 1 or 2 NRSTS. Low-risk patients were treated with surgery alone. Intermediate-/high-risk patients received ifosfamide/doxorubicin and radiotherapy, with definitive resection either before or after 12 weeks of chemoradiotherapy. RESULTS: Estimated 5-year event-free and overall survival were 90% and 100% low risk (n = 80), 55% and 78% intermediate risk (n = 15), and 25% and 25% high risk (n = 4). In low-risk patients, only local recurrence was seen in 10%; none with margins greater than 1 mm recurred locally. Sixteen of 17 intermediate-/high-risk patients who completed neoadjuvant chemoradiotherapy underwent gross total tumor resection, 80% with negative margins. Intermediate-/high-risk group events included one local and seven metastatic recurrences. Had the FNCLCC grading system been used to direct treatment, 29% of low-risk (surgery alone) patients would have received radiotherapy ± chemotherapy. CONCLUSIONS: Most low-risk patients with completely resected POG low-grade NRSTS are successfully treated with surgery alone, and surgical margins greater than 1 mm may be sufficient to prevent local recurrence. Patients with intermediate- and high-risk low-grade NRSTS have outcomes similar to patients with high-grade histology, and require more effective therapies. Use of the current FNCLCC grading system may result in overtreatment of low-risk NRSTS curable with surgery alone.

Amputation surgery associated with shortened survival in patients with localized soft tissue sarcoma.

BACKGROUND: The objectives of this study were to clarify whether localized extremity soft tissue sarcoma (STS) patients who underwent amputation surgery experienced worsened survival and to identify those patients for whom amputation surgery worsened survival. METHODS: Using the Surveillance, Epidemiology, and End Results database, we identified 8897 patients with localized extremity STS between 1983 and 2016. Of these 6431 patients, 733 patients underwent amputation surgery (Amputation group), and 5698 underwent limb-sparing surgery (Limb-sparing group). RESULTS: After adjusting for patient background by propensity score matching, a total of 1346 patients were included. Patients in the Amputation group showed worsened survival (cancer-specific survival (CSS): hazard ratio (HR) = 1.42, 95% confidence interval (CI) 1.15-1.75, overall survival (OS): HR = 1.41, 95%CI 1.20-1.65). In subclass analysis, patients with high-grade STS, spindle cell sarcoma and liposarcoma in the Amputation group showed shortened survival (high-grade-CSS: HR = 1.44, 95%CI 1.16-1.77, OS: HR = 1.38, 95%CI 1.18-1.62; spindle cell sarcoma-CSS: HR = 4.75, 95%CI 1.56-14.4, OS: HR = 2.32, 95%CI 1.45-3.70; liposarcoma-CSS: HR = 2.91, 95%CI 1.54-5.50, OS: HR = 2.32, 95%CI 1.45-3.70). CONCLUSIONS: Survival was shortened in localized extremity STS patients who received amputation surgery.

Radiomic Features of the Edema Region May Contribute to Grading Meningiomas With Peritumoral Edema.

BACKGROUND: Meningiomas are frequently accompanied by peritumoral edema (PTE). The potential value of radiomic features of edema region in meningioma grading has not been investigated. PURPOSE: To investigate whether radiomic features of edema region contribute to grading meningiomas with PTE. STUDY TYPE: Retrospective. POPULATION: A total of 444 patients including 196 grade II and 248 WHO grade I meningiomas: 356 patients for training, 88 for validation. FIELD STRENGTH/SEQUENCE: A 1.5-T/3.0-T, noncontrast T1-weighted (T1WI), T2-weighted (T2WI), contrast-enhanced T1-weighted (T1CE) spin echo sequences. ASSESSMENT: A total of 851 radiomic features were extracted from each sequence on each region (tumor and edema region). These features were integrated by region respectively. Three subsets of clinical-radiomic features were constructed by joining clinical information (sex, age, tumor volume, and edema volume) and radiomic features of three regions: tumor, edema, and combined subsets. For each subset, features were filtered by the least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. Top 20 features of each subset were finally selected. STATISTICAL TESTS: Stochastic Gradient Boosting, Random Forest, and Bagged AdaBoost predictive models were built based on each subset. Discriminative abilities of models were quantified using receiver operating characteristics (ROC) and the area under the curve (AUC). A P value < 0.05 was considered statistically significant. RESULTS: Random Forest model based on combined subset (AUC [95% CI] = 0.880 [0.807-0.953]) had the best discriminative ability in grading meningiomas among the final models. The best model of edema subset and tumor subset were Random Forest model (AUC [95% CI] = 0.864 [0.791-0.938]) and Stochastic Gradient Boosting model (AUC [95% CI] = 0.844 [0.760-0.928]), respectively. DATA CONCLUSION: Radiomic features of edema region may contribute to grading meningiomas with PTE. The Random Forest model based on combined subset surpasses the best model based on tumor or edema subset regarding grading meningiomas with PTE. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.

BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored. PURPOSE: To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria. STUDY TYPE: Retrospective. SUBJECTS: A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria. FIELD STRENGTH/SEQUENCE: Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery. ASSESSMENT: Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis). STATISTICAL TESTS: We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05. RESULTS: In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis. DATA CONCLUSION: ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

MRI proton density fat fraction for estimation of tumor grade in steatotic hepatocellular carcinoma.

OBJECTIVES: Image-based detection of intralesional fat in focal liver lesions has been established in diagnostic guidelines as a feature indicative of hepatocellular carcinoma (HCC) and associated with a favorable prognosis. Given recent advances in MRI-based fat quantification techniques, we investigated a possible relationship between intralesional fat content and histologic tumor grade in steatotic HCCs. METHODS: Patients with histopathologically confirmed HCC and prior MRI with proton density fat fraction (PDFF) mapping were retrospectively identified. Intralesional fat of HCCs was assessed using an ROI-based analysis and the median fat fraction of steatotic HCCs was compared between tumor grades G1-3 with non-parametric testing. ROC analysis was performed in case of statistically significant differences (p < 0.05). Subgroup analyses were conducted for patients with/without liver steatosis and with/without liver cirrhosis. RESULTS: A total of 57 patients with steatotic HCCs (62 lesions) were eligible for analysis. The median fat fraction was significantly higher for G1 lesions (median [interquartile range], 7.9% [6.0─10.7%]) than for G2 (4.4% [3.2─6.6%]; p = .001) and G3 lesions (4.7% [2.8─7.8%]; p = .036). PDFF was a good discriminator between G1 and G2/3 lesions (AUC .81; cut-off 5.8%, sensitivity 83%, specificity 68%) with comparable results in patients with liver cirrhosis. In patients with liver steatosis, intralesional fat content was higher than in the overall sample, with PDFF performing better in distinguishing between G1 and G2/3 lesions (AUC .92; cut-off 8.8%, sensitivity 83%, specificity 91%). CONCLUSIONS: Quantification of intralesional fat using MRI PDFF mapping allows distinction between well- and less-differentiated steatotic HCCs. CLINICAL RELEVANCE: PDFF mapping may help optimize precision medicine as a tool for tumor grade assessment in steatotic HCCs. Further investigation of intratumoral fat content as a potential prognostic indicator of treatment response is encouraged. KEY POINTS: • MRI proton density fat fraction mapping enables distinction between well- (G1) and less- (G2 and G3) differentiated steatotic hepatocellular carcinomas. • In a retrospective single-center study with 62 histologically proven steatotic hepatocellular carcinomas, G1 tumors showed a higher intralesional fat content than G2 and G3 tumors (7.9% vs. 4.4% and 4.7%; p = .004). • In liver steatosis, MRI proton density fat fraction mapping was an even better discriminator between G1 and G2/G3 steatotic hepatocellular carcinomas.

MR-based simplified extraprostatic extension evaluation: comparison of performances of different predictive models.

OBJECTIVES: To test reproducibility and predictive value of a simplified score for assessment of extraprostatic tumor extension (sEPE grade). METHODS: Sixty-five patients (mean age ± SD, 67 years ± 6.3) treated with radical prostatectomy for prostate cancer who underwent 1.5-Tesla multiparametric magnetic resonance imaging (mpMRI) 6 months before surgery were enrolled. sEPE grade was derived from mpMRI metrics: curvilinear contact length > 15 mm (CCL) and capsular bulging/irregularity. The diameter of the index lesion (dIL) was also measured. Evaluations were independently performed by seven radiologists, and inter-reader agreement was tested by weighted Cohen K coefficient. A nested (two levels) Monte Carlo cross-validation was used. The best cut-off value for dIL was selected by means of the Youden J index to classify values into a binary variable termed dIL*. Logistic regression models based on sEPE grade, dIL, and clinical scores were developed to predict pathologic EPE. Results on validation set were assessed by the main metrics of the receiver operating characteristics curve (ROC) and by decision curve analysis (DCA). Based on our findings, we defined and tested an alternative sEPE grade formulation. RESULTS: Pathologic EPE was found in 31/65 (48%) patients. Average κw was 0.65 (95% CI 0.51-0.79), 0.66 (95% CI 0.48-0.84), 0.67 (95% CI 0.50-0.84), and 0.43 (95% CI 0.22-0.63) for sEPE grading, CLL ≥ 15 mm, dIL*, and capsular bulging/irregularity, respectively. The highest diagnostic yield in predicting EPE was obtained by combining both sEPE grade and dIL*(ROC-AUC 0.81). CONCLUSIONS: sEPE grade is reproducible and when combined with the dIL* accurately predicts extraprostatic tumor extension. KEY POINTS: • Simple and reproducible mpMRI semi-quantitative scoring system for extraprostatic tumor extension. • sEPE grade accurately predicts extraprostatic tumor extension regardless of reader expertise. • Accurate pre-operative staging and risk stratification for optimized patient management.

A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists.

OBJECTIVES: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS: • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.

Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging.

OBJECTIVES: To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas. METHODS: Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed. RESULTS: The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05). CONCLUSIONS: Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models. KEY POINTS: • Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade.

OBJECTIVES: To develop and validate a radiomics-based model (ADGGIP) for predicting adult-type diffuse gliomas (ADG) grade by combining multiple diffusion modalities and clinical and imaging morphologic features. METHODS: In this prospective study, we recruited 103 participants diagnosed with ADG and collected their preoperative conventional MRI and multiple diffusion imaging (diffusion tensor imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and mean apparent propagator diffusion-MRI) data in our hospital, as well as clinical information. Radiomic features of the diffusion images and clinical information and morphological data from the radiological reports were extracted, and multiple pipelines were used to construct the optimal model. Model validation was performed through a time-independent validation cohort. ROC curves were used to evaluate model performance. The clinical benefit was determined by decision curve analysis. RESULTS: From June 2018 to May 2021, 72 participants were recruited for the training cohort. Between June 2021 and February 2022, 31 participants were enrolled in the prospective validation cohort. In the training cohort (AUC 0.958), internal validation cohort (0.942), and prospective validation cohort (0.880), ADGGIP had good accuracy in predicting ADG grade. ADGGIP was also significantly better than the single-modality prediction model (AUC 0.860) and clinical imaging morphology model (0.841) (all p < .01) in the prospective validation cohort. When the threshold probability was greater than 5%, ADGGIP provided the greatest net benefit. CONCLUSION: ADGGIP, which is based on advanced diffusion modalities, can predict the grade of ADG with high accuracy and robustness and can help improve clinical decision-making. CLINICAL RELEVANCE STATEMENT: Integrated multi-modal predictive modeling is beneficial for early detection and treatment planning of adult-type diffuse gliomas, as well as for investigating the genuine clinical significance of biomarkers. KEY POINTS: • Integrated model exhibits the highest performance and stability. • When the threshold is greater than 5%, the integrated model has the greatest net benefit. • The advanced diffusion models do not demonstrate better performance than the simple technology.

Association of CT findings with invasive subtypes and the new grading system of lung adenocarcinoma.

AIM: To predict the differentiation between invasive growth patterns and new grades of lung adenocarcinoma (LAC) using computed tomography (CT). MATERIALS AND METHODS: The CT features of 180 surgically treated LAC patients were compared retrospectively to pathological invasive subtypes and tumour grades as defined by the new grading system published in 2021 by the World Health Organization. Two radiologists reviewed the images semi-quantitatively and independently. Univariable and multivariable regression models were built from the statistical means of their assessments to predict invasive subtypes and grades. The area under the curve (AUC) calculation was used to select the best models. The Youden index was applied to determine the cut-off values for radiological parameters. RESULTS: The acinar/papillary patterns were associated with ill-defined margins, lower consolidation/tumour ratio and air bronchogram. The solid growth pattern was associated with a well-defined margin and hypodensity, and the micropapillary (MP) subtype with spiculation. From Grades 1 to 3, the amount of air bronchogram decreased and the consolidation/tumour ratio increased. In the sub-analyses, the best model for differentiating Grade 2 from Grade 1 had the following CT features: solid/subsolid type, consolidation/tumour ratio, well-defined margin, and air bronchogram (AUC = 0.783) and Grade 3 from Grade 2: size of the consolidation part/whole tumour ratio, size of the consolidation part, and well-defined margin (AUC = 0.759). The interobserver agreements between the two radiologists varied between 0.67 and 0.98. CONCLUSIONS: Air bronchogram, consolidation/tumour ratio, and well-defined margin are among the best imaging findings to discriminate between both invasive subtypes and the new grades in LAC.

Evaluating the Outcomes of Active Surveillance in Grade Group 2 Prostate Cancer: Prospective Results from the Canary PASS Cohort.

PURPOSE: Active surveillance (AS) for grade group (GG) 2 patients is not yet well defined. We sought to compare clinical outcomes of men with GG1 and GG2 prostate cancer undergoing AS in a large prospective North American cohort. MATERIALS AND METHODS: Participants were prospectively enrolled in an AS study with protocol-directed followup at 10 centers in the U.S. and Canada. We evaluated time from diagnosis to biopsy grade reclassification and time to treatment. In men treated after initial surveillance, adverse pathology and recurrence were also analyzed. RESULTS: At diagnosis, 154 (9%) had GG2 and 1,574 (91%) had GG1. Five-year reclassification rates were similar between GG2 and GG1 (30% vs 37%, p=0.11). However, more patients with GG2 were treated at 5 years (58% vs 34%, p <0.001) and GG at diagnosis was associated with time to treatment (HR=1.41; p=0.01). Treatment rates were similar in patients who reclassified during AS, but in patients who did not reclassify, those diagnosed with GG2 underwent definitive treatment more often than GG1 (5-year treatment rates 52% and 12%, p <0.0001). In participants who underwent radical prostatectomy after initial surveillance, the adjusted risk of adverse pathology was similar (HR=1.26; p=0.4). Biochemical recurrence within 3 years of treatment for GG2 and GG1 patients was 6% for both groups. CONCLUSIONS: In patients on AS, the rate of definitive treatment is higher after an initial diagnosis of GG2 than GG1. Adverse pathology after radical prostatectomy and short-term biochemical recurrence after definitive treatment were similar between GG2 and GG1.

Oncologic Outcomes of Total Length Gleason Pattern 4 on Biopsy in Men with Grade Group 2 Prostate Cancer.

PURPOSE: Gleason Score 7 prostate cancer comprises a wide spectrum of disease risk, and precise substratification is paramount. Our group previously demonstrated that the total length of Gleason pattern (GP) 4 is a better predictor than %GP4 for adverse pathological outcomes at radical prostatectomy. We aimed to determine the association of GP4 length on prostate biopsy with post-prostatectomy oncologic outcomes. MATERIALS AND METHODS: We compared 4 GP4 quantification methods-including maximum %GP4 in any single core, overall %GP4, total length GP4 (mm) across all cores and length GP4 (mm) in the highest volume core-for prediction of biochemical recurrence-free survival after radical prostatectomy using multivariable Cox proportional hazards regression. RESULTS: A total of 457 men with grade group 2 prostate cancer on biopsy subsequently underwent radical prostatectomy. The 3-year biochemical recurrence-free survival probability was 85% (95% CI 81-88). On multivariable analysis, all 4 GP4 quantification methods were associated with biochemical recurrence-maximum %GP4 (HR=1.30; 95% CI 1.07-1.59; p=0.009), overall %GP4 (HR=1.61; 95% CI 1.21-2.15; p=0.001), total length GP4 (HR=2.48; 95% CI 1.36-4.52; p=0.003) and length GP4 in highest core (HR=1.32; 95% CI 1.11-1.57; p=0.001). However, we were unable to identify differences between methods of quantification with a relatively low event rate. CONCLUSIONS: These findings support further studies on GP4 quantification in addition to the ratio of GP3 and GP4 to classify prostate cancer risk. Research should also be conducted on whether GP4 quantification could provide a surrogate endpoint for disease progression for trials in active surveillance.

Precision-guidance vs Systematic Sampling: Optimizing Biopsy Assessment of Secondary Prostate Cancer Suspicious Multiparametric Magnetic Resonance Imaging Lesions.

PURPOSE: We assessed the diagnostic yield of consecutive transperineal targeted biopsy of multiparametric magnetic resonance imaging index lesion and secondary lesion and additive systematic biopsy in patients who received combined targeted biopsy+systematic biopsy of prostate. MATERIALS AND METHODS: Of 1,467 patients with targeted biopsy+systematic biopsy, analyses were restricted to 571 patients with index lesion+secondary lesion, Prostate Imaging-Reporting and Data System score ≥3. Index lesion was defined as having the greatest Prostate Imaging-Reporting and Data System score and/or lesion volume as opposed to secondary lesion. We retrospectively compared clinically significant prostate cancer rates (ie, Gleason Grade Group ≥2) between index lesion+secondary lesion and index lesion+secondary lesion+systematic biopsy. Subgroup analyses in men with ipsilateral index lesion+secondary lesion focused on contralateral systematic biopsy. Multivariable logistic regression analyses to predict any clinically significant prostate cancer included age, previous biopsies, prostate specific antigen density, respective index lesion/secondary lesion volumes, side relation, Prostate Imaging-Reporting and Data System strata, and number of targeted biopsy and systematic biopsy cores. RESULTS: Clinically significant prostate cancer rates for index lesion+secondary lesion vs index lesion+secondary lesion+systematic biopsy were 38% vs 42% (P = .2) at expense of significantly higher median number of biopsy cores (9 vs 25, P < .001). In the subgroup with ipsilateral index lesion+secondary lesion (n = 236), contralateral systematic biopsy detected clinically significant prostate cancer in 17%. In the narrower subgroup with ipsilateral index lesion+secondary lesion (n = 131) without any clinically significant prostate cancer, contralateral systematic biopsy detected clinically significant prostate cancer in 3.8%. Multivariable logistic regression analyses confirmed contralateral systematic biopsy as independent predictor, but performed similarly without systematic biopsy information (area under the curve 87.1% vs 86.6%). CONCLUSIONS: Targeted biopsy of secondary lesion should be included in targeted biopsy protocols due to added diagnostic information. However, for targeted biopsy of index lesion+secondary lesion additional systematic biopsy is of limited informative value in terms of overall clinically significant prostate cancer detection. However, when index lesion+secondary lesion are ipsilateral, contralateral systematic biopsy should be recommended for purpose of prostate lobe information. Our results indicate great potential to reduce systematic biopsy cores and associated potential morbidity, and warrant prospective evaluation.

Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study.

OBJECTIVES: Multiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade. METHODS: We retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements. RESULTS: Fractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2-5) cancer with a sensitivity of 91% (confidence interval [CI]: 83-96%) and a specificity of 86% (CI: 73-94%). FD correlated linearly with ISUP groups (r2 = 0.874, p < 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1-4 (p ≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUCFD = 0.97 versus AUCADC = 0.77, p < 0.001). CONCLUSION: Fractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors. KEY POINTS: • In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1-4). • Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83-96%) and a specificity of 86% (73-94%). • Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading.

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.

OBJECTIVES: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.

Accuracy of fractal analysis and PI-RADS assessment of prostate magnetic resonance imaging for prediction of cancer grade groups: a clinical validation study.

OBJECTIVES: Multiparametric MRI with Prostate Imaging Reporting and Data System (PI-RADS) assessment is sensitive but not specific for detecting clinically significant prostate cancer. This study validates the diagnostic accuracy of the recently suggested fractal dimension (FD) of perfusion for detecting clinically significant cancer. MATERIALS AND METHODS: Routine clinical MR imaging data, acquired at 3 T without an endorectal coil including dynamic contrast-enhanced sequences, of 72 prostate cancer foci in 64 patients were analyzed. In-bore MRI-guided biopsy with International Society of Urological Pathology (ISUP) grading served as reference standard. Previously established FD cutoffs for predicting tumor grade were compared to measurements of the apparent diffusion coefficient (25th percentile, ADC25) and PI-RADS assessment with and without inclusion of the FD as separate criterion. RESULTS: Fractal analysis allowed prediction of ISUP grade groups 1 to 4 but not 5, with high agreement to the reference standard (κFD = 0.88 [CI: 0.79-0.98]). Integrating fractal analysis into PI-RADS allowed a strong improvement in specificity and overall accuracy while maintaining high sensitivity for significant cancer detection (ISUP > 1; PI-RADS alone: sensitivity = 96%, specificity = 20%, area under the receiver operating curve [AUC] = 0.65; versus PI-RADS with fractal analysis: sensitivity = 95%, specificity = 88%, AUC = 0.92, p < 0.001). ADC25 only differentiated low-grade group 1 from pooled higher-grade groups 2-5 (κADC = 0.36 [CI: 0.12-0.59]). Importantly, fractal analysis was significantly more reliable than ADC25 in predicting non-significant and clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75, p < 0.001). Diagnostic accuracy was not significantly affected by zone location. CONCLUSIONS: Fractal analysis is accurate in noninvasively predicting tumor grades in prostate cancer and adds independent information when implemented into PI-RADS assessment. This opens the opportunity to individually adjust biopsy priority and method in individual patients. KEY POINTS: • Fractal analysis of perfusion is accurate in noninvasively predicting tumor grades in prostate cancer using dynamic contrast-enhanced sequences (κFD = 0.88). • Including the fractal dimension into PI-RADS as a separate criterion improved specificity (from 20 to 88%) and overall accuracy (AUC from 0.86 to 0.96) while maintaining high sensitivity (96% versus 95%) for predicting clinically significant cancer. • Fractal analysis was significantly more reliable than ADC25 in predicting clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75).

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.

OBJECTIVE: To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. METHODS: This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed. RESULTS: Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI. CONCLUSION: Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. KEY POINTS: • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.