Background

Whole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans.

Methods

We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient's T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020).

Results

Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53-13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated.

Conclusions

An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

Purpose

Standard of care for definitive treatment of locally advanced cervical cancer (LACC) is concurrent chemoradiation followed by a brachytherapy boost. Only 55.8% of women in the United States receive brachytherapy, with even lower proportions in San Diego and Imperial Counties. The purpose of this study was to investigate brachytherapy practice and referral patterns in Western United States border region.

Material and methods

A short survey was sent to 28 radiation oncologists in San Diego and Imperial Counties, who treat patients with gynecologic malignancies. Descriptive statistics were used for analysis.

Results

Seventeen (61%) physicians responded to the survey. All physicians reported some training in cervical cancer brachytherapy during residency, with median 6 months. Only two physicians reported personally treating all cervical cancer patients with brachytherapy; however, 92% of remaining physicians would recommend brachytherapy for patients if given time and access. The most common reason for referral (78%) was patients deemed to require hybrid or interstitial brachytherapy implants. Barriers to referral included patients' preference, insurance status, their resources, or logistics. No changes were reported for brachytherapy practices during the COVID-19 pandemic, except the addition of pre-procedural testing for SARS-CoV-2. Ninety-two percent of physicians identified inadequate maintenance of skills as a barrier to performing brachytherapy, but 77% were not interested in additional training. External beam radiation therapy boosts were rarely recommended in case scenarios describing potentially curable patients.

Conclusions

The importance of brachytherapy is widely recognized for conferring a survival benefit, but barriers to implementation include inadequate training or maintenance of skills, and larger systematic issues related to reimbursement policy, social support, and financial hardship. As most established providers were uninterested in additional brachytherapy training, future approaches to improve patients' access should be multidimensional and reflect the value of brachytherapy in definitive treatment of patients with LACC.

Purpose

Comorbidity is an independent predictor of mortality and treatment tolerance in head and neck cancer and should be considered with regard to treatment intensification. Multiple previously validated models can be used to evaluate comorbidity and propensity to benefit from intensive treatment, but they have not been directly compared.

Materials and methods

An online tool was developed and used to calculate the Charlson Comorbidity Index (CCI), Adult Comorbidity Evaluation-27 (ACE-27), Cumulative Illness Rating Scale for Geriatrics (CIRS-G), Geriatric 8 (G8), Cancer and Aging Research Group (CARG), and Generalized Competing Event (GCE) scores. To assess interrater variability, five evaluators independently calculated scores on a retrospective cohort of 20 patients. Correlation between models as well as age and performance status were calculated from a cohort of 40 patients.

Results

The GCE and G8 models had an excellent (intraclass correlation coefficient and Fleiss' kappa ≥ 0.75) degree of interrater agreement. The CCI, ACE-27, CIRS-G, and CARG had a good (intraclass correlation coefficient and Fleiss' kappa 0.6-0.74) degree of interrater agreement. There was statistically significant correlation between models, especially with the CCI, ACE-27, and CIRS-G indices. Increased age was correlated with an increased CCI score and having moderate to severe comorbidity was correlated with the ACE-27 model. Except for the G8 model, the comorbidity indices were not associated with Eastern Cooperative Oncology Group performance status.

Conclusion

We developed an online tool to calculate indices of comorbidity in patients with head and neck cancer with a high degree of reproducibility. Comorbidity is not strongly correlated with performance status and should be independently evaluated in patients.

Background

Locally advanced cervical cancer (CC) remains lethal in the United States. We investigate the effect of receiving care at an National Cancer Institute-designated cancer center (NCICC) on survival.

Methods

Data for women diagnosed with CC from 2004 to 2016 who received radiation treatment were extracted from the California Cancer Registry (n = 4250). Cox proportional hazards regression models assessed whether (1) receiving care at NCICCs was associated with risk of CC-specific death, (2) this association remained after multivariable adjustment for age, race/ethnicity, and insurance status, and (3) this association was explained by receipt of guideline-concordant treatment.

Results

Median age was 50 years (interquartile range [IQR] 41-61 years), with median follow-up of 2.7 years (IQR 1.3-6.0 years). One-third of patients were seen at an NCICC, and 29% died of CC. The hazard of CC-specific death was reduced by 20% for those receiving care at NCICCs compared with patients receiving care elsewhere (HR = .80; 95% CI, 0.70-0.90). Adjustment for guideline-concordant treatment and other covariates minimally attenuated the association to 0.83 (95% CI, 0.74-0.95), suggesting that the survival advantage associated with care at NCICCs may not be due to receipt of guideline-concordant treatment.

Conclusions

This study demonstrates survival benefit for patients receiving care at NCICCs compared with those receiving care elsewhere that is not explained by differences in guideline-concordant care. Structural, organizational, or provider characteristics and differences in patients receiving care at centers with and without NCI designation could explain observed associations. Further understanding of these factors will promote equality across oncology care facilities and survival equity for patients with CC.

Background

Optimal prostate cancer (PCa) screening strategies will focus on men likely to have potentially lethal disease. Age-specific incidence rates (ASIRs) by modern clinical risk groups could inform risk stratification efforts for screening.

Methods

This cross-sectional population study identified all men diagnosed with PCa in Norway from 2014 to 2017 (n = 20,356). Age, Gleason score (primary plus secondary), and clinical stage were extracted. Patients were assigned to clinical risk groups: low, favorable intermediate, unfavorable intermediate, high, regional, and metastatic. Chi-square tests analyzed the independence of Gleason scores and modern PCa risk groups with age. ASIRs for each risk group were calculated as the product of Norwegian ASIRs for all PCa and the proportions observed for each risk category.

Results

Older age was significantly associated with a higher Gleason score and more advanced disease. The percentages of men with Gleason 8 to 10 disease among men aged 55 to 59, 65 to 69, 75 to 79, and 85 to 89 years were 16.5%, 23.4%, 37.2%, and 59.9%, respectively (P < .001); the percentages of men in the same age groups with at least high-risk disease were 29.3%, 39.1%, 60.4%, and 90.6%, respectively (P < .001). The maximum ASIRs (per 100,000 men) for low-risk, favorable intermediate-risk, unfavorable intermediate-risk, high-risk, regional, and metastatic disease were 157.1 for those aged 65 to 69 years, 183.8 for those aged 65 to 69 years, 194.8 for those aged 70 to 74 years, 408.3 for those aged 75 to 79 years, 159.7 for those aged ≥85 years, and 314.0 for those aged ≥85 years, respectively. At the ages of 75 to 79 years, the ASIR of high-risk disease was approximately 6 times greater than the ASIR at 55 to 59 years.

Conclusions

The risk of clinically significant localized PCa increases with age. Healthy older men may benefit from screening.

Background

Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI₄ ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI₄ -C₁ , yielded greatest tumor conspicuity.

Purpose

To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI₄ -C₁ ) as a quantitative voxel-level classifier of PCa.

Study type

Retrospective.

Subjects

Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.

Field strength/sequence

A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.

Assessment

High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI₄ -C₁ and conventional ADC. Classifier images were also generated.

Statistical tests

Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI₄ -C₁ was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR₉₀ ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.

Results

RSI₄ -C₁ outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR₉₀ (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).

Data conclusion

RSI₄ -C₁ yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.

Evidence level

3 TECHNICAL EFFICACY: Stage 2.

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Background

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

Purpose

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

Study type

Retrospective.

Subjects

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

Field strength/sequence

3T multishell diffusion-weighted sequence.

Assessment

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

Statistical tests

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

Results

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

Data conclusion

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T₂-weighted images were annotated. Changes in distance between DWI- and T₂-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T₂-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10^-20). On average (± SD), participants' largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0-7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10^-12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

Abstract: Purpose: Multiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa. Materials and Methods: Retrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone. Results: 151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (p<0.0001) were superior to ADC for patient-level detection of csPCa. The performance of RSIrs was comparable to that of PI-RADS (p=0.6). AUC for PI-RADS+RSIrs was 0.84 [0.77, 0.90], superior to PI-RADS or RSIrs, alone (p=0.008, p=0.009). Conclusions: RSIrs was superior to conventional ADC and comparable to (routine, clinical) PI-RADS for patient-level detection of csPCa. The combination of PI-RADS and RSIrs was superior to either alone. RSIrs is a promising quantitative marker worthy of prospective study in the setting of csPCa detection. Disclosures: MEH reports honoraria from Multimodal Imaging Services Corporation and research funding from General Electric Healthcare. AMD is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. and receives funding through research agreements with General Electric Healthcare. The terms of these arrangements have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. TMS reports honoraria from Multimodal Imaging Services Corporation, Varian Medical Systems, and WebMD; he has an equity interest in CorTechs Labs, Inc. and also serves on its Scientific Advisory Board. These companies might potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies.