Social Determinants of Long-Term Opioid Use Following Total Knee Arthroplasty

Total knee arthroplasty (TKA) risks persistent pain and long-term opioid use (LTO). The role of social determinants of health (SDoH) in LTO is not well established. We hypothesized that SDoH would be associated with postsurgical LTO after controlling for relevant demographic and clinical variables. This study utilized data from the Veterans Affairs Surgical Quality Improvement Program, VA Corporate Data Warehouse, and Centers for Medicare and Medicaid Services, including Veterans aged ≥ 65 who underwent elective TKA between 2013 and 2019 with no postsurgical complications or history of significant opioid use. LTO was defined as > 90 days of opioid use beginning within 90 days postsurgery. SDoH variables included the Area Deprivation Index, rurality, and housing instability in the last 12 months identified via medical record screener or International Classification of Diseases, Tenth Revision codes. Multivariable risk adjustment models controlled for demographic and clinical characteristics. Of the 9,064 Veterans, 97% were male, 84.2% white, mean age was 70.6 years, 46.3% rural, 11.2% living in highly deprived areas, and 0.9% with a history of homelessness/housing instability. Only 3.7% (n = 336) developed LTO following TKA. In a logistic regression model of only SDoH variables, housing instability (odds ratio [OR] = 2.38, 95% confidence interval [CI]: 1.09-5.22) and rurality conferred significant risk for LTO. After adjusting for demographic and clinical variables, LTO was only associated with increasing days of opioid supply in the year prior to surgery (OR = 1.52, 95% CI: 1.43-1.63 per 30 days) and the initial opioid fill (OR = 1.07; 95% CI: 1.06-1.08 per day). Our primary hypothesis was not supported; however, our findings do suggest that patients with housing instability may present unique challenges for postoperative pain management and be at higher risk for LTO.

Presentation Acuity and Surgical Outcomes for Patients With Health Insurance Living in Highly Deprived Neighborhoods

Importance

Insurance coverage expansion has been proposed as a solution to improving health disparities, but insurance expansion alone may be insufficient to alleviate care access barriers.

Objective

To assess the association of Area Deprivation Index (ADI) with postsurgical textbook outcomes (TO) and presentation acuity for individuals with private insurance or Medicare.

Design, Setting, and Participants

This cohort study used data from the National Surgical Quality Improvement Program (2013-2019) merged with electronic health record data from 3 academic health care systems. Data were analyzed from June 2022 to August 2023.

Exposure

Living in a neighborhood with an ADI greater than 85.

Main Outcomes and Measures

TO, defined as absence of unplanned reoperations, Clavien-Dindo grade 4 complications, mortality, emergency department visits/observation stays, and readmissions, and presentation acuity, defined as having preoperative acute serious conditions (PASC) and urgent or emergent cases.

Results

Among a cohort of 29 924 patients, the mean (SD) age was 60.6 (15.6) years; 16 424 (54.9%) were female, and 13 500 (45.1) were male. A total of 14 306 patients had private insurance and 15 618 had Medicare. Patients in highly deprived neighborhoods (5536 patients [18.5%]), with an ADI greater than 85, had lower/worse odds of TO in both the private insurance group (adjusted odds ratio [aOR], 0.87; 95% CI, 0.76-0.99; P = .04) and Medicare group (aOR, 0.90; 95% CI, 0.82-1.00; P = .04) and higher odds of PASC and urgent or emergent cases. The association of ADIs greater than 85 with TO lost significance after adjusting for PASC and urgent/emergent cases. Differences in the probability of TO between the lowest-risk (ADI ≤85, no PASC, and elective surgery) and highest-risk (ADI >85, PASC, and urgent/emergent surgery) scenarios stratified by frailty were highest for very frail patients (Risk Analysis Index ≥40) with differences of 40.2% and 43.1% for those with private insurance and Medicare, respectively.

Conclusions and Relevance

This study found that patients living in highly deprived neighborhoods had lower/worse odds of TO and higher presentation acuity despite having private insurance or Medicare. These findings suggest that insurance coverage expansion alone is insufficient to overcome health care disparities, possibly due to persistent barriers to preventive care and other complex causes of health inequities.

Outcomes of Women Undergoing Noncardiac Surgery in Veterans Affairs Compared With Non-Veterans Affairs Care Settings

Importance

Recent legislation facilitates veterans' ability to receive non-Veterans Affairs (VA) surgical care. Although veterans are predominantly male, the number of women receiving care within the VA has nearly doubled to 10% over the past decade and recent data comparing the surgical care of women in VA and non-VA care settings are lacking.

Objective

To compare postoperative outcomes among women treated in VA hospitals vs private-sector hospitals.

Design, Setting, and Participants

This coarsened exact-matched cohort study across 9 noncardiac specialties in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) and American College of Surgeons National Surgical Quality Improvement Program (NSQIP) took place from January 1, 2016, to December 31, 2019. Multivariable Poisson models with robust standard errors were used to evaluate the association between VA vs private-sector care settings and 30-day mortality. Hospitals participating in American College of Surgeons NSQIP and VASQIP were included. Data analysis was performed in January 2023. Participants included female patients 18 years old or older.

Exposures

Surgical care in VA or private-sector hospitals.

Main Outcomes and Measures

Postoperative 30-day mortality and failure to rescue (FTR).

Results

Among 1 913 033 procedures analyzed, patients in VASQIP were younger (VASQIP: mean age, 49.8 [SD, 13.0] years; NSQIP: mean age, 55.9 [SD, 16.9] years; P < .001) and although most patients in both groups identified as White, there were significantly more Black women in VASQIP compared with NSQIP (29.6% vs 12.7%; P < .001). The mean risk analysis index score was lower in VASQIP (13.9 [SD, 6.4]) compared with NSQIP (16.3 [SD, 7.8]) (P < .001 for both). Patients in the VA were more likely to have a preoperative acute serious condition (2.4% vs 1.8%: P < .001), but cases in NSQIP were more frequently emergent (6.9% vs 2.6%; P < .001). The 30-day mortality, complications, and FTR were 0.2%, 3.2%, and 0.1% in VASQIP (n = 36 762 procedures) as compared with 0.8%, 5.0%, and 0.5% in NSQIP (n = 1 876 271 procedures), respectively (all P < .001). Among 1 763 540 matched women (n = 36 478 procedures in VASQIP; n = 1 727 062 procedures in NSQIP), these rates were 0.3%, 3.7%, and 0.2% in NSQIP and 0.1%, 3.4%, and 0.1% in VASQIP (all P < .01). Relative to private-sector care, VA surgical care was associated with a lower risk of death (adjusted risk ratio [aRR], 0.41; 95% CI, 0.23-0.76). This finding was robust among women undergoing gynecologic surgery, inpatient surgery, and low-physiologic stress procedures. VA surgical care was also associated with lower risk of FTR (aRR, 0.41; 95% CI, 0.18-0.92) for frail or Black women and inpatient and low-physiologic stress procedures.

Conclusions and Relevance

Although women comprise the minority of veterans receiving care within the VA, in this study, VA surgical care for women was associated with half the risk of postoperative death and FTR. The VA appears better equipped to meet the unique surgical needs and risk profiles of veterans, regardless of sex and health policy decisions, including funding, should reflect these important outcome differences.

A Surgical Desirability of Outcome Ranking (DOOR) Reveals Complex Relationships Between Race/Ethnicity, Insurance Type, and Neighborhood Deprivation

OBJECTIVE

Develop an ordinal Desirability of Outcome Ranking (DOOR) for surgical outcomes to examine complex associations of Social Determinants of Health.

BACKGROUND

Studies focused on single or binary composite outcomes may not detect health disparities.

METHODS

Three health care system cohort study using NSQIP (2013-2019) linked with EHR and risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status and operative stress assessing associations of multilevel Social Determinants of Health of race/ethnicity, insurance type (Private 13,957; Medicare 15,198; Medicaid 2835; Uninsured 2963) and Area Deprivation Index (ADI) on DOOR and the binary Textbook Outcomes (TO).

RESULTS

Patients living in highly deprived neighborhoods (ADI>85) had higher odds of PASC [adjusted odds ratio (aOR)=1.13, CI=1.02-1.25, P <0.001] and urgent/emergent cases (aOR=1.23, CI=1.16-1.31, P <0.001). Increased odds of higher/less desirable DOOR scores were associated with patients identifying as Black versus White and on Medicare, Medicaid or Uninsured versus Private insurance. Patients with ADI>85 had lower odds of TO (aOR=0.91, CI=0.85-0.97, P =0.006) until adjusting for insurance. In contrast, patients with ADI>85 had increased odds of higher DOOR (aOR=1.07, CI=1.01-1.14, P <0.021) after adjusting for insurance but similar odds after adjusting for PASC and urgent/emergent cases.

CONCLUSIONS

DOOR revealed complex interactions between race/ethnicity, insurance type and neighborhood deprivation. ADI>85 was associated with higher odds of worse DOOR outcomes while TO failed to capture the effect of ADI. Our results suggest that presentation acuity is a critical determinant of worse outcomes in patients in highly deprived neighborhoods and without insurance. Including risk adjustment for living in deprived neighborhoods and urgent/emergent surgeries could improve the accuracy of quality metrics.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Who is afraid of Hermy and Jimmy? Relating to and normalizing psychosis through theater

OBJECTIVE

Psychotic experiences, such as hearing voices that others do not hear, being afraid of threats that others do not perceive, or believing in ideas that others find implausible can be confusing for those who face them and challenging to relate to for those who do not, leading to alienation and social exclusion. The objective of this article is to discuss how immersion in theater can enhance our understanding of human nature and facilitate a social environment that supports the recovery of individuals with psychosis.

METHODS

Drawing on theories of the psychology of art and narrative psychology, this conceptual article discusses a theatrical production, a play, titled "Voices," created by a person with lived experience of voice hearing. We apply Semenov's model of art as a social psychological system as a guiding framework to focus on the roles of the art product, artist-author, artist-performer, and recipient.

RESULTS

Theater is a uniquely reciprocal art form where actors and spectators share emotional, intellectual, and cathartic experiences, which could foster interpersonal connection, personal growth, and empathy. This article brings new perspective on how theater can elucidate psychotic experiences, encourage dialogue about these experiences, and facilitate social integration and recovery of individuals living with psychosis.

CONCLUSION AND IMPLICATIONS FOR PRACTICE

Theater can promote social change, making space for a wider range of perspectives in society. Engaging individuals with lived experiences of psychosis in theatrical productions could lead to new insights about and acceptance of psychotic experiences, both for these individuals and for society at large. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differentiating Urgent from Elective Cases Matters in Minority Populations: Developing an Ordinal "Desirability of Outcome Ranking" to Increase Granularity and Sensitivity of Surgical Outcomes Assessment

BACKGROUND

Surgical analyses often focus on single or binary outcomes; we developed an ordinal Desirability of Outcome Ranking (DOOR) for surgery to increase granularity and sensitivity of surgical outcome assessments. Many studies also combine elective and urgent procedures for risk adjustment. We used DOOR to examine complex associations of race/ethnicity and presentation acuity.

STUDY DESIGN

NSQIP (2013 to 2019) cohort study assessing DOOR outcomes across race/ethnicity groups risk-adjusted for frailty, operative stress, preoperative acute serious conditions, and elective, urgent, and emergent cases.

RESULTS

The cohort included 1,597,199 elective, 340,350 urgent, and 185,073 emergent cases with patient mean age of 60.0 ± 15.8, and 56.4% of the surgeries were performed on female patients. Minority race/ethnicity groups had increased odds of presenting with preoperative acute serious conditions (adjusted odds ratio [aORs] range 1.22 to 1.74), urgent (aOR range 1.04 to 2.21), and emergent (aOR range 1.15 to 2.18) surgeries vs the White group. Black (aOR range 1.23 to 1.34) and Native (aOR range 1.07 to 1.17) groups had increased odds of higher/worse DOOR outcomes; however, the Hispanic group had increased odds of higher/worse DOOR (aOR 1.11, CI 1.10 to 1.13), but decreased odds (aORs range 0.94 to 0.96) after adjusting for case status; the Asian group had better outcomes vs the White group. DOOR outcomes improved in minority groups when using elective vs elective/urgent cases as the reference group.

CONCLUSIONS

NSQIP surgical DOOR is a new method to assess outcomes and reveals a complex interplay between race/ethnicity and presentation acuity. Combining elective and urgent cases in risk adjustment may penalize hospitals serving a higher proportion of minority populations. DOOR can be used to improve detection of health disparities and serves as a roadmap for the development of other ordinal surgical outcomes measures. Improving surgical outcomes should focus on decreasing preoperative acute serious conditions and urgent and emergent surgeries, possibly by improving access to care, especially for minority populations.

Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19

COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings. For automated assessment of pulmonary findings, we have developed an unsupervised learning technique termed the calculated lung ultrasound (CLU) index. The CLU can quantify various types of lung findings, such as A or B lines, consolidations, and pleural effusions, and it uses these findings to calculate a CLU index score, which is a quantitative measure of pulmonary disease burden. This is accomplished using an unsupervised, patient-specific approach that does not require training on a large dataset. The CLU was tested on 52 lung ultrasound examinations from several institutions. CLU demonstrated excellent concordance with radiologist findings in different pulmonary disease states. Given the global nature of COVID-19, the CLU would be useful for sonographers and physicians in resource-strapped areas with limited ultrasound training and diagnostic capacities for more accurate assessment of pulmonary status.

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82-0.94), and AUC-PR of 0.94 (95% CI: 0.87-0.97).

Association of Cumulative Colorectal Surgery Hospital Costs, Readmissions, and Emergency Department/Observation Stays with Insurance Type

BACKGROUND/PURPOSE

Medicare's Hospital Readmission Reduction Program disproportionately penalizes safety-net hospitals (SNH) caring for vulnerable populations. This study assessed the association of insurance type with 30-day emergency department visits/observation stays (EDOS), readmissions, and cumulative costs in colorectal surgery patients.

METHODS

Retrospective inpatient cohort study using the National Surgical Quality Improvement Program (2013-2019) with cost data in a SNH. The odds of EDOS and readmissions and cumulative variable (index hospitalization and all 30-day EDOS and readmissions) costs were modeled adjusting for frailty, case status, presence of a stoma, and open versus laparoscopic surgery.

RESULTS

The cohort had 245 private, 195 Medicare, and 590 Medicaid/uninsured cases, with a mean age 55.0 years (SD = 13.3) and 52.9% of the cases were performed on male patients. Most cases were open surgeries (58.7%). Complication rates were 41.8%, EDOS 12.0%, and readmissions 20.1%. Medicaid/uninsured had increased odds of urgent/emergent surgeries (aOR = 2.15, CI = 1.56-2.98, p < 0.001) and complications (aOR = 1.43, CI = 1.02-2.03, p = 0.042) versus private patients. Medicaid/uninsured versus private patients had higher EDOS (16.6% versus 4.1%) and readmissions (22.9% versus 14.3%) rates and higher odds of EDOS (aOR = 4.81, CI = 2.57-10.06, p < 0.001), and readmissions (aOR = 1.62, CI = 1.07-2.50, p = 0.025), while Medicare patients had similar odds versus private. Cumulative variable cost %change was increased for Medicare and Medicaid/uninsured, but Medicaid/uninsured was similar to private after adjusting for urgent/emergent cases.

CONCLUSIONS

Increased urgent/emergent cases in Medicaid/uninsured populations drive increased complications odds and higher costs compared to private patients, suggesting lack of access to outpatient care. SNH care for higher cost populations, receive lower reimbursements, and are penalized by value-based programs. Increasing healthcare access for Medicaid/uninsured patients could reduce urgent/emergent surgeries, resulting in fewer complications, EDOS/readmissions, and costs.

Association of Insurance Type with Inpatient Surgical 30-day Readmissions, Emergency Department Visits/Observation Stays, and Costs

OBJECTIVE

To assess the association of Private, Medicare, and Medicaid/Uninsured insurance type with 30-day Emergency Department visits/Observation Stays (EDOS), readmissions, and costs in a safety-net hospital (SNH) serving diverse socioeconomic status patients.

SUMMARY BACKGROUND DATA

Medicare's Hospital Readmission Reduction Program (HRRP) disproportionately penalizes SNHs.

METHODS

This retrospective cohort study used inpatient National Surgical Quality Improvement Program (2013-2019) data merged with cost data. Frailty, expanded Operative Stress Score, case status, and insurance type were used to predict odds of EDOS and readmissions, as well as index hospitalization costs.

RESULTS

The cohort had 1,477 Private; 1,164 Medicare; and 3,488 Medicaid/Uninsured cases with a patient mean age 52.1 years [SD=14.7] and 46.8% of the cases were performed on male patients. Medicaid/Uninsured (aOR=2.69, CI=2.38-3.05, P<.001) and Medicare (aOR=1.32, CI=1.11-1.56, P=.001) had increased odds of urgent/emergent surgeries and complications versus Private patients. Despite having similar frailty distributions, Medicaid/Uninsured compared to Private patients had higher odds of EDOS (aOR=1.71, CI=1.39-2.11, P<.001), and readmissions (aOR=1.35, CI=1.11-1.65, P=.004), after adjusting for frailty, OSS, and case status, while Medicare patients had similar odds of EDOS and readmissions versus Private. Hospitalization variable cost %change was increased for Medicare (12.5%) and Medicaid/Uninsured (5.9%), but Medicaid/Uninsured was similar to Private after adjusting for urgent/emergent cases.

CONCLUSIONS

Increased rates and odds of urgent/emergent cases in Medicaid/Uninsured patients drive increased odds of complications and index hospitalization costs versus Private. SNHs care for higher cost populations while receiving lower reimbursements and are further penalized by the unintended consequences of HRRP. Increasing access to care, especially for Medicaid/Uninsured patients, could reduce urgent/emergent surgeries resulting in fewer complications, EDOS/readmissions, and costs.

Independent Associations of Neighborhood Deprivation and Patient-level Social Determinants of Health with Textbook Outcomes after Inpatient Surgery

Objective

Assess associations of Social Determinants of Health (SDoH) using Area Deprivation Index (ADI), race/ethnicity and insurance type with Textbook Outcomes (TO).

Summary Background Data

Individual- and contextual-level SDoH affect health outcomes, but only one SDoH level is usually included.

Methods

Three healthcare system cohort study using National Surgical Quality Improvement Program (2013-2019) linked with ADI risk-adjusted for frailty, case status and operative stress examining TO/TO components (unplanned reoperations, complications, mortality, Emergency Department/Observation Stays and readmissions).

Results

Cohort (34,251 cases) mean age 58.3 [SD=16.0], 54.8% females, 14.1% Hispanics, 11.6% Non-Hispanic Blacks, 21.6% with ADI>85, and 81.8% TO. Racial and ethnic minorities, non-Private insurance, and ADI>85 patients had increased odds of urgent/emergent surgeries (aORs range: 1.17-2.83, all P<.001). Non-Hispanic Black patients, ADI>85 and non-Private insurances had lower TO odds (aORs range: 0.55-0.93, all P<.04), but ADI>85 lost significance after including case status. Urgent/emergent versus elective had lower TO odds (aOR=0.51, P<.001). ADI>85 patients had higher complication and mortality odds. Estimated reduction in TO probability was 9.9% (CI=7.2%-12.6%) for urgent/emergent cases, 7.0% (CI=4.6%-9.3%) for Medicaid, and 1.6% (CI=0.2%-3.0%) for non-Hispanic Black patients. TO probability difference for lowest-risk (White-Private-ADI≤85-elective) to highest-risk (Black-Medicaid-ADI>85-urgent/emergent) was 29.8% for very frail patients.

Conclusion

Multi-level SDoH had independent effects on TO, predominately affecting outcomes through increased rates/odds of urgent/emergent surgeries driving complications and worse outcomes. Lowest-risk versus highest-risk scenarios demonstrated the magnitude of intersecting SDoH variables. Combination of insurance type and ADI should be used to identify high-risk patients to redesign care pathways to improve outcomes. Risk adjustment including contextual neighborhood deprivation and patient-level SDoH could reduce unintended consequences of value-based programs.

Cost of Failure to Achieve Textbook Outcomes: Association of Insurance Type with Outcomes and Cumulative Cost for Inpatient Surgery

BACKGROUND

Surgical outcome/cost analyses typically focus on single outcomes and do not include encounters beyond the index hospitalization.

STUDY DESIGN

This cohort study used NSQIP (2013-2019) data with electronic health record and cost data risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status, and operative stress assessing cumulative costs of failure to achieve textbook outcomes defined as absence of 30-day Clavien-Dindo level III and IV complications, emergency department visits/observation stays (EDOS), and readmissions across insurance types (private, Medicare, Medicaid, uninsured). Return costs were defined as costs of all 30-day emergency department visits/observation stays and readmissions.

RESULTS

Cases were performed on patients (private 1,506; Medicare 1,218; Medicaid 1,420; uninsured 2,178) with a mean age 52.3 years (SD 14.7) and 47.5% male. Medicaid and uninsured patients had higher odds of presenting with preoperative acute serious conditions (adjusted odds ratios 1.89 and 1.81, respectively) and undergoing urgent/emergent surgeries (adjusted odds ratios 2.23 and 3.02, respectively) vs private. Medicaid and uninsured patients had lower odds of textbook outcomes (adjusted odds ratios 0.53 and 0.78, respectively) and higher odds of emergency department visits/observation stays and readmissions vs private. Not achieving textbook outcomes was associated with a greater than 95.1% increase in cumulative costs. Medicaid patients had a relative increase of 23.1% in cumulative costs vs private, which was 18.2% after adjusting for urgent/emergent cases. Return costs were 37.5% and 65.8% higher for Medicaid and uninsured patients, respectively, vs private.

CONCUSIONS

Higher costs for Medicaid patients were partially driven by increased presentation acuity (increased rates/odds of preoperative acute serious conditions and urgent/emergent surgeries) and higher rates of multiple emergency department visits/observation stays and readmission occurrences. Decreasing surgical costs/improving outcomes should focus on reducing urgent/emergent surgeries and improving postoperative care coordination, especially for Medicaid and uninsured populations.

Retrospective cohort study comparing surgical inpatient charges, total costs, and variable costs as hospital cost savings measures

We analyzed differences (charges, total, and variable costs) in estimating cost savings of quality improvement projects using reduction of serious/life-threatening complications (Clavien-Dindo Level IV) and insurance type (Private, Medicare, and Medicaid/Uninsured) to evaluate the cost measures. Multiple measures are used to analyze hospital costs and compare cost outcomes across health systems with differing patient compositions. We used National Surgical Quality Improvement Program inpatient (2013-2019) with charge and cost data in a hospital serving diverse socioeconomic status patients. Simulation was used to estimate variable costs and total costs at 3 proportions of fixed costs (FC). Cases (Private 1517; Medicare 1224; Medicaid/Uninsured 3648) with patient mean age 52.3 years (Standard Deviation = 14.7) and 47.3% male. Medicare (adjusted odds ratio = 1.55, 95% confidence interval = 1.16-2.09, P = .003) and Medicaid/Uninsured (adjusted odds ratio = 1.41, 95% confidence interval = 1.10-1.82, P = .008) had higher odds of complications versus Private. Medicaid/Uninsured had higher relative charges versus Private, while Medicaid/Uninsured and Medicare had higher relative variable and total costs versus Private. Targeting a 15% reduction in serious complications for robust patients undergoing moderate-stress procedures estimated variable cost savings of $286,392. Total cost saving estimates progressively increased with increasing proportions of FC; $443,943 (35% FC), $577,495 (50% FC), and $1184,403 (75% FC). In conclusion, charges did not identify increased costs for Medicare versus Private patients. Complications were associated with > 200% change in costs. Surgical hospitalizations for Medicare and Medicaid/Uninsured patients cost more than Private patients. Variable costs should be used to avoid overestimating potential cost savings of quality improvement interventions, as total costs include fixed costs that are difficult to change in the short term.

Association of Insurance Type With Colorectal Surgery Outcomes and Costs at a Safety-Net Hospital: A Retrospective Observational Study

Association of insurance type with colorectal surgical complications, textbook outcomes (TO), and cost in a safety-net hospital (SNH).

Background

SNHs have higher surgical complications and costs compared to low-burden hospitals. How does presentation acuity and insurance type influence colorectal surgical outcomes?

Methods

Retrospective cohort study using single-site National Surgical Quality Improvement Program (2013-2019) with cost data and risk-adjusted by frailty, preoperative serious acute conditions (PASC), case status and open versus laparoscopic to evaluate 30-day reoperations, any complication, Clavien-Dindo IV (CDIV) complications, TO, and hospitalization variable costs.

Results

Cases (Private 252; Medicare 207; Medicaid/Uninsured 619) with patient mean age 55.2 years (SD = 13.4) and 53.1% male. Adjusting for frailty, open abdomen, and urgent/emergent cases, Medicaid/Uninsured patients had higher odds of presenting with PASC (adjusted odds ratio [aOR] = 2.02, 95% confidence interval [CI] = 1.22-3.52, P = 0.009) versus Private. Medicaid/Uninsured (aOR = 1.80, 95% CI = 1.28-2.55, P < 0.001) patients were more likely to undergo urgent/emergent surgeries compared to Private. Medicare patients had increased odds of any and CDIV complications while Medicaid/Uninsured had increased odds of any complication, emergency department or observations stays, and readmissions versus Private. Medicare (aOR = 0.51, 95% CI = 0.33-0.88, P = 0.003) and Medicaid/Uninsured (aOR = 0.43, 95% CI = 0.30-0.60, P < 0.001) patients had lower odds of achieving TO versus Private. Variable cost %change increased in Medicaid/Uninsured patients to 13.94% (P = 0.005) versus Private but was similar after adjusting for case status. Urgent/emergent cases (43.23%, P < 0.001) and any complication (78.34%, P < 0.001) increased %change hospitalization costs.

Conclusions

Decreasing the incidence of urgent/emergent colorectal surgeries, possibly by improving access to care, could have a greater impact on improving clinical outcomes and decreasing costs, especially in Medicaid/Uninsured insurance type patients.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness

BACKGROUND

Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.

PURPOSE

To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.

STUDY TYPE

Prospective.

POPULATION

Thirty-three patients prospectively imaged prior to prostatectomy.

FIELD STRENGTH/SEQUENCE

3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence.

ASSESSMENT

Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).

STATISTICAL TEST

Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.

RESULTS

The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.

DATA CONCLUSION

We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 3.

Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.

Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery

Background

Stereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort.

Methods

Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T₁ post-contrast (T₁c) and T₂ fluid-attenuated inversion recovery (T₂-FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort (N = 95) and validated in the remaining cases (N = 40) using surgical pathology as the gold standard.

Results

One hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T₁c, and 4 T₂-FLAIR features had an AUC of 0.77 (95% confidence interval [CI]: 0.66–0.88), sensitivity of 67% and specificity of 86% in the training cohort, and AUC of 0.71 (95% CI: 0.51–0.91), sensitivity of 52% and specificity of 90% in the validation cohort.

Conclusions

MRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.

Multiparametric magnetic resonance imaging to characterize cabotegravir long-acting formulation depot kinetics in healthy adult volunteers

AIM

Cabotegravir long-acting (LA) intramuscular (IM) injection is being investigated for HIV preexposure prophylaxis due to its potent antiretroviral activity and infrequent dosing requirement. A subset of healthy adult volunteers participating in a Phase I study assessing cabotegravir tissue pharmacokinetics underwent serial magnetic resonance imaging (MRI) to assess drug depot localization and kinetics following a single cabotegravir LA IM targeted injection.

METHODS

Eight participants (four men, four women) were administered cabotegravir LA 600 mg under ultrasonographic-guided injection targeting the gluteal muscles. MRI was performed to determine injection-site location in gluteal muscle (IM), subcutaneous (SC) adipose tissue and combined IM/SC compartments, and to quantify drug depot characteristics, including volume and surface area, on Days 1 (≤2 hours postinjection), 3 and 8. Linear regression analysis examined correlations between MRI-derived parameters and plasma cabotegravir exposure metrics, including maximum observed concentration (C_max ) and partial area under the concentration-time curve (AUC) through Weeks 4 and 8.

RESULTS

Cabotegravir LA depot locations varied by participant and were identified in the IM compartment (n = 2), combined IM/SC compartments (n = 4), SC compartment (n = 1) and retroperitoneal cavity (n = 1). Although several MRI parameter and exposure metric correlations were determined, total depot surface area on Day 1 strongly correlated with plasma cabotegravir concentration at Days 3 and 8, C_max and partial AUC through Weeks 4 and 8.

CONCLUSION

MRI clearly delineated cabotegravir LA injection-site location and depot kinetics in healthy adults. Although injection-site variability was observed, drug depot surface area correlated with both plasma C_max and partial AUC independently of anatomical distribution.

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

MATERIALS AND METHODS

This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based U-Net that used a training set of 25,920 images and validation set of 16,416 images. Three musculoskeletal radiologists reviewed 88 knee MR studies in 2 sessions, the original (proton density [PD] + FSPD) and the synthetic (PD + AFSMRI). Readers recorded AFSMRI quality (diagnostic/nondiagnostic) and the presence or absence of meniscal, ligament, and tendon tears; cartilage defects; and bone marrow abnormalities. Contrast-to-noise rate measurements were made among subcutaneous fat, fluid, bone marrow, cartilage, and muscle. The original MRI sequences were used as the reference standard to determine the diagnostic performance of AFSMRI (combined with the original PD sequence). This is a fully balanced study design, where all readers read all images the same number of times, which allowed the determination of the interchangeability of the original and synthetic protocols. Descriptive statistics, intermethod agreement, interobserver concordance, and interchangeability tests were applied. A P value less than 0.01 was considered statistically significant for the likelihood ratio testing, and P value less than 0.05 for all other statistical analyses.

RESULTS

Artificial intelligence-based FS MRI quality was rated as diagnostic (98.9% [87/88] to 100% [88/88], all readers). Diagnostic performance (sensitivity/specificity) of the synthetic protocol was high, for tears of the menisci (91% [71/78], 86% [84/98]), cruciate ligaments (92% [12/13], 98% [160/163]), collateral ligaments (80% [16/20], 100% [156/156]), and tendons (90% [9/10], 100% [166/166]). For cartilage defects and bone marrow abnormalities, the synthetic protocol offered an overall sensitivity/specificity of 77% (170/221)/93% (287/307) and 76% (95/125)/90% (443/491), respectively. Intermethod agreement ranged from moderate to substantial for almost all evaluated structures (menisci, cruciate ligaments, collateral ligaments, and bone marrow abnormalities). No significant difference was observed between methods for all structural abnormalities by all readers (P > 0.05), except for cartilage assessment. Interobserver agreement ranged from moderate to substantial for almost all evaluated structures. Original and synthetic protocols were interchangeable for the diagnosis of all evaluated structures. There was no significant difference for the common exact match proportions for all combinations (P > 0.01). The conspicuity of all tissues assessed through contrast-to-noise rate was higher on AFSMRI than on original FSPD images (P < 0.05).

CONCLUSIONS

Artificial intelligence-based FS MRI (3D AFSMRI) is feasible and offers a method for fast imaging, with similar detection rates for structural abnormalities of the knee, compared with original 3D MR sequences.

A phase Ib/IIa, open-label, multiple ascending-dose trial of domagrozumab in fukutin-related protein limb-girdle muscular dystrophy

INTRODUCTION/AIMS

In this study we report the results of a phase Ib/IIa, open-label, multiple ascending-dose trial of domagrozumab, a myostatin inhibitor, in patients with fukutin-related protein (FKRP)-associated limb-girdle muscular dystrophy.

METHODS

Nineteen patients were enrolled and assigned to one of three dosing arms (5, 20, or 40 mg/kg every 4 weeks). After 32 weeks of treatment, participants receiving the lowest dose were switched to the highest dose (40 mg/kg) for an additional 32 weeks. An extension study was also conducted. The primary endpoints were safety and tolerability. Secondary endpoints included muscle strength, timed function testing, pulmonary function, lean body mass, pharmacokinetics, and pharmacodynamics. As an exploratory outcome, muscle fat fractions were derived from whole-body magnetic resonance images.

RESULTS

Serum concentrations of domagrozumab increased in a dose-dependent manner and modest levels of myostatin inhibition were observed in both serum and muscle tissue. The most frequently occurring adverse events were injuries secondary to falls. There were no significant between-group differences in the strength, functional, or imaging outcomes studied.

DISCUSSION

We conclude that, although domagrozumab was safe in patients in limb-girdle muscular dystrophy type 2I/R9, there was no clear evidence supporting its efficacy in improving muscle strength or function.

Brain metabolites in cholinergic and glutamatergic pathways are altered by pancreatic cancer cachexia

BACKGROUND

Cachexia is a major cause of morbidity in pancreatic ductal adenocarcinoma (PDAC) patients. Our purpose was to understand the impact of PDAC-induced cachexia on brain metabolism in PDAC xenograft studies, to gain new insights into the causes of cachexia-induced morbidity. Changes in mouse and human plasma metabolites were characterized to identify underlying causes of brain metabolic changes.

METHODS

We quantified metabolites, detected with high-resolution ¹ H magnetic resonance spectroscopy, in the brain and plasma of normal mice (n = 10) and mice bearing cachexia (n = 10) or non-cachexia (n = 9) inducing PDAC xenografts as well as in human plasma obtained from normal individuals (n = 24) and from individuals with benign pancreatic disease (n = 20) and PDAC (n = 20). Statistical significance was defined as a P value ≤0.05.

RESULTS

The brain metabolic signature of cachexia-inducing PDAC was characterized by a significant depletion of choline of -27% and -21% as well as increases of glutamine of 13% and 9% and formate of 21% and 14%, relative to normal controls and non-cachectic tumour-bearing mice, respectively. Good to moderate correlations with percent weight change were found for choline (r = 0.70), glutamine (r = -0.58), and formate (r = -0.43). Significant choline depletion of -38% and -30%, relative to normal controls and non-cachectic tumour-bearing mice, respectively, detected in the plasma of cachectic mice likely contributed to decreased brain choline in cachectic mice. Similarly, relative to normal controls and patients with benign disease, choline levels in human plasma samples of PDAC patients were significantly lower by -12% and -20% respectively. A comparison of plasma metabolites from PDAC patients with and without weight loss identified significant changes in glutamine metabolism.

CONCLUSIONS

Disturbances in metabolites of the choline/cholinergic and glutamine/glutamate/glutamatergic neurotransmitter pathways may contribute to morbidity. Metabolic normalization may provide strategies to reduce morbidity. The human plasma metabolite changes observed may lead to the development of companion diagnostic markers to detect PDAC and PDAC-induced cachexia.

Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10-3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy

Background

Pathogenic variants in the FKRP gene cause impaired glycosylation of α-dystroglycan in muscle, producing a limb-girdle muscular dystrophy with cardiomyopathy. Despite advances in understanding the pathophysiology of FKRP-associated myopathies, clinical research in the limb-girdle muscular dystrophies has been limited by the lack of normative biomarker data to gauge disease progression.

Methods

Participants in a phase 2 clinical trial were evaluated over a 4-month, untreated lead-in period to evaluate repeatability and to obtain normative data for timed function tests, strength tests, pulmonary function, and body composition using DEXA and whole-body MRI. Novel deep learning algorithms were used to analyze MRI scans and quantify muscle, fat, and intramuscular fat infiltration in the thighs. T-tests and signed rank tests were used to assess changes in these outcome measures.

Results

Nineteen participants were observed during the lead-in period for this trial. No significant changes were noted in the strength, pulmonary function, or body composition outcome measures over the 4-month observation period. One timed function measure, the 4-stair climb, showed a statistically significant difference over the observation period. Quantitative estimates of muscle, fat, and intramuscular fat infiltration from whole-body MRI corresponded significantly with DEXA estimates of body composition, strength, and timed function measures.

Conclusions

We describe normative data and repeatability performance for multiple physical function measures in an adult FKRP muscular dystrophy population. Our analysis indicates that deep learning algorithms can be used to quantify healthy and dystrophic muscle seen on whole-body imaging.

Trial registration

This study was retrospectively registered in clinicaltrials.gov (NCT02841267) on July 22, 2016 and data supporting this study has been submitted to this registry.

Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging

BACKGROUND AND PURPOSE

Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets.

METHODS

We developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p < 0.05.

RESULTS

The mpRad features successfully classified malignant from benign breast lesions with excellent sensitivity and specificity of 82.5% and 80.5%, respectively, with Area Under the receiver operating characteristic Curve (AUC) of 0.87 (0.81-0.93). mpRad provided a 9-28% increase in AUC metrics over single radiomic parameters.

CONCLUSIONS

We have introduced the mpRad framework that extends radiomic analysis from single images to multiparametric datasets for better characterization of the underlying tissue biology.

Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results

PURPOSE

Deep learning is emerging in radiology due to the increased computational capabilities available to reading rooms. These computational developments have the ability to mimic the radiologist and may allow for more accurate tissue characterization of normal and pathological lesion tissue to assist radiologists in defining different diseases. We introduce a novel tissue signature model based on tissue characteristics in breast tissue from multiparametric magnetic resonance imaging (mpMRI). The breast tissue signatures are used as inputs in a stacked sparse autoencoder (SSAE) multiparametric deep learning (MPDL) network for segmentation of breast mpMRI.

METHODS

We constructed the MPDL network from SSAE with 5 layers with 10 nodes at each layer. A total cohort of 195 breast cancer subjects were used for training and testing of the MPDL network. The cohort consisted of a training dataset of 145 subjects and an independent validation set of 50 subjects. After segmentation, we used a combined SAE-support vector machine (SAE-SVM) learning method for classification. Dice similarity (DS) metrics were calculated between the segmented MPDL and dynamic contrast enhancement (DCE) MRI-defined lesions. Sensitivity, specificity, and area under the curve (AUC) metrics were used to classify benign from malignant lesions.

RESULTS

The MPDL segmentation resulted in a high DS of 0.87 ± 0.05 for malignant lesions and 0.84 ± 0.07 for benign lesions. The MPDL had excellent sensitivity and specificity of 86% and 86% with positive predictive and negative predictive values of 92% and 73%, respectively, and an AUC of 0.90.

CONCLUSIONS

Using a new tissue signature model as inputs into the MPDL algorithm, we have successfully validated MPDL in a large cohort of subjects and achieved results similar to radiologists.

Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis

PURPOSE

Patients with recurrent abdominal pain and pancreatic enzyme elevations may be diagnosed clinically with recurrent acute pancreatitis (RAP) even with normal imaging or no imaging at all. Since neither abdominal pain nor enzyme elevations are specific for acute pancreatitis (AP), and patients with RAP often have a normal appearing pancreas on CT after resolution of an AP episode, RAP diagnosis can be challenging. This study aims to determine if quantitative radiomic features of the pancreas on CT can differentiate patients with functional abdominal pain, RAP, and chronic pancreatitis (CP).

METHOD

Contrast enhanced CT abdominal images of adult patients evaluated in a pancreatitis clinic from 2010 to 2018 with the diagnosis of RAP, functional abdominal pain, or CP were retrospectively reviewed. The pancreas was outlined by drawing region of interest (ROI) on images. 54 radiomic features were extracted from each ROI and were compared between the patient groups. A one-vs-one Isomap and Support Vector Machine (IsoSVM) classifier was also trained and tested to classify patients into one of the three diagnostic groups based on their radiomic features.

RESULTS

Among the study's 56 patients, 20 (35.7 %) had RAP, 19 (33.9 %) had functional abdominal pain, and 17 (30.4 %) had CP. On univariate analysis, 11 radiomic features (10 GLCM features and one NGTDM feature) were significantly different between the patient groups. The IsoSVM classifier for prediction of patient diagnosis had an overall accuracy of 82.1 %.

CONCLUSIONS

Certain radiomic features on CT imaging can differentiate patients with functional abdominal pain, RAP, and CP.

Deep learning and radiomics in precision medicine

INTRODUCTION

The radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue characteristics. The goal is to use integrated deep learning and radiomics with radiological parameters to produce a personalized diagnosis for a patient.

AREAS COVERED

This review provides an overview of historical and current deep learning and radiomics methods in the context of precision medicine in radiology. A literature search for 'Deep Learning', 'Radiomics', 'Machine learning', 'Artificial Intelligence', 'Convolutional Neural Network', 'Generative Adversarial Network', 'Autoencoders', Deep Belief Networks", Reinforcement Learning", and 'Multiparametric MRI' was performed in PubMed, ArXiv, Scopus, CVPR, SPIE, IEEE Xplore, and NIPS to identify articles of interest.

EXPERT OPINION

In conclusion, both deep learning and radiomics are two rapidly advancing technologies that will unite in the future to produce a single unified framework for clinical decision support with a potential to completely revolutionize the field of precision medicine.

Measurement Repeatability of 18F-FDG PET/CT Versus 18F-FDG PET/MRI in Solid Tumors of the Pelvis

Knowledge of the within-subject variability of ¹⁸F-FDG PET/MRI measurements is necessary for proper interpretation of quantitative PET or MRI metrics in the context of therapeutic efficacy assessments with integrated PET/MRI scanners. The goal of this study was to determine the test–retest repeatability of these metrics on PET/MRI, with comparison to similar metrics acquired by PET/CT. Methods: This prospective study enrolled subjects with pathology-proven pelvic malignancies. Baseline imaging consisted of PET/CT immediately followed by PET/MRI, using a single 370-MBq ¹⁸F-FDG dose. Repeat imaging was performed within 7 d using an identical imaging protocol, with no oncologic therapy between sessions. PET imaging on both scanners consisted of a list-mode acquisition at a single pelvic station. The MRI consisted of 2-point Dixon imaging for attenuation correction, standard sequences for anatomic correlation, and diffusion-weighted imaging. PET data were statically reconstructed using various frame durations and minimizing uptake time differences between sessions. SUV metrics were extracted for both PET/CT and PET/MRI in each imaging session. Apparent diffusion coefficient (ADC) metrics were extracted for both PET/MRI sessions. Results: The study cohort consisted of 14 subjects (13 female, 1 male) with various pelvic cancers (11 cervical, 2 rectal, 1 endometrial). For SUV_max, the within-subject coefficient of variation (wCV) appeared higher for PET/CT (8.5%–12.8%) than PET/MRI (6.6%–8.7%) across all PET reconstructions, though with no significant repeatability differences (all P values ≥ 0.08) between modalities. For lean body mass-adjusted SUV_peak, the wCVs appeared similar for PET/CT (9.9%–11.5%) and PET/MRI (9.2%–11.3%) across all PET reconstructions, again with no significant repeatability differences (all P values ≥ 0.14) between modalities. For PET/MRI, the wCV for ADC_median of 3.5% appeared lower than the wCVs for SUV_max (6.6%–8.7%) and SUL_peak (9.2%–11.3%), though without significant repeatability differences (all P values ≥ 0.23). Conclusion: For solid tumors of the pelvis, the repeatability of the evaluated SUV and ADC metrics on ¹⁸F-FDG PET/MRI is both acceptably high and similar to previously published values for ¹⁸F-FDG PET/CT and MRI, supporting the use of ¹⁸F-FDG PET/MRI for quantitative oncologic treatment response assessments.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics

PURPOSE

Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional imaging. In this study, we sought to apply a radiomics-based prediction model to the problem of diagnosing treatment effect after SRS.

METHODS AND MATERIALS

We included patients in the Johns Hopkins Health System who were treated with SRS for brain metastases who subsequently underwent resection for symptomatic growth. We also included cases of likely treatment effect in which lesions grew but subsequently regressed spontaneously. Lesions were segmented semiautomatically on preoperative T1 postcontrast and T2 fluid-attenuated inversion recovery magnetic resonance imaging, and radiomic features were extracted with software developed in-house. Top-performing features on univariate logistic regression were entered into a hybrid feature selection/classification model, IsoSVM, with parameter optimization and further feature selection performed using leave-one-out cross-validation. Final model performance was assessed by 10-fold cross-validation with 100 repeats. All cases were independently reviewed by a board-certified neuroradiologist for comparison.

RESULTS

We identified 82 treated lesions across 66 patients, with 77 lesions having pathologic confirmation. There were 51 radiomic features extracted per segmented lesion on each magnetic resonance imaging sequence. An optimized IsoSVM classifier based on top-ranked radiomic features had sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%.

CONCLUSIONS

Radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.

Multiparametric Whole-body MRI with Diffusion-weighted Imaging and ADC Mapping for the Identification of Visceral and Osseous Metastases From Solid Tumors

RATIONALE AND OBJECTIVES

The purpose of this study was to investigate the use of multiparametric, whole-body, diffusion-weighted imaging (WB-DWI) and apparent diffusion coefficient (ADC) maps with T₂-weighted magnetic resonance imaging (MRI) at 3T for the detection and monitoring of metastatic disease in patients.

MATERIALS AND METHODS

Fifty-four participants (32 healthy subjects and 22 patients) were scanned with WB-DWI methods using a 3T MRI scanner. Axial, sagittal, or coronal fat-suppressed T₂-weighted (T₂WI), T₁-weighted (T₁WI), and DWI images were acquired. Total MRI acquisition and set-up time was approximately 45 minutes. Metastatic disease on MRI was confirmed based on T₂WI characteristics. The number of lesions was established on computed tomography (CT) or positron emission tomography (PET-CT). Whole-body ADC maps and T₂WI were constructed, and region-of-interests were drawn in normal and abnormal-appearing tissue for quantitative analysis. Statistical analysis was performed using a paired t tests and P < .05 was considered statistically significant.

RESULTS

There were 91 metastatic lesions detected from the CT or PET-CT with a missed recurrent lesion in the prostate. Multiparametric WB-MRI had excellent sensitivity (96%) for detection of metastatic lesions compared to CT. ADC map values and the ADC ratio in metastatic bone lesions were significantly increased (P < .05) compared to normal bone. In soft tissue, ADC map values and ratios in metastatic lesions were decreased compared to normal soft tissue.

CONCLUSION

We have demonstrated that multiparametric WB-MRI is feasible for oncologic staging to identify bony and visceral metastasis in breast, prostate, pancreatic, and colorectal cancers. WB-MRI can be tailored to fit the patient, such that an "individualized patient sequence" can be developed for a comprehensive evaluation for staging and response during treatment.

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI

Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors. We tested our radiomic feature mapping framework on a retrospective cohort of 124 patients (26 benign and 98 malignant) who underwent multiparametric breast MR imaging at 3 T. The MRI parameters used were T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI). The RFMs were computed by convolving MRI images with statistical filters based on first order statistics and gray level co-occurrence matrix features. Malignant lesions demonstrated significantly higher entropy on both post contrast DCE-MRI (Benign-DCE entropy: 5.72 ± 0.12, Malignant-DCE entropy: 6.29 ± 0.06, p = 0.0002) and apparent diffusion coefficient (ADC) maps as compared to benign lesions (Benign-ADC entropy: 5.65 ± 0.15, Malignant ADC entropy: 6.20 ± 0.07, p = 0.002). There was no significant difference between glandular tissue entropy values in the two groups. Furthermore, the RFMs from DCE-MRI and DWI demonstrated significantly different RFM curves for benign and malignant lesions indicating their correlation to tumor vascular and cellular heterogeneity respectively. There were significant differences in the quantitative MRI metrics of ADC and perfusion. The multiview IsoSVM model classified benign and malignant breast tumors with sensitivity and specificity of 93 and 85%, respectively, with an AUC of 0.91.

An automated system for analyzing magnetic resonance imaging (MRI) can differentiate benign from malignant breast tumors with high accuracy. Vishwa S. Parekh and Michael A. Jacobs 
from Johns Hopkins University School of Medicine in Baltimore, Maryland, USA, developed an algorithm for extracting textural information from MRI scans that are not visually perceivable to radiologists using machine learning and Radiomic features. Their model combines different MRI parameters to produce so-called radiomic feature maps. The researchers tested their mapping framework on a retrospective cohort of 124 patients, 26 of whom had benign breast lesions and 98 had malignant tumors. They found statistical differences in certain MRI and radiomic metrics. Moreover, they demonstrated quantitative ADC map values and Dynamic contrast pharmacokinetic modeling to characterize the radiomic features. Overall, the method identified a breast lesion as benign or malignant with 93% sensitivity and 85% specificity, suggesting that radiomic feature mapping could aid in diagnosing and characterizing the disease correctly and tailoring therapy accordingly.

Current whole-body MRI applications in the neurofibromatoses: NF1, NF2, and schwannomatosis

OBJECTIVES

The Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) International Collaboration Whole-Body MRI (WB-MRI) Working Group reviewed the existing literature on WB-MRI, an emerging technology for assessing disease in patients with neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), and schwannomatosis (SWN), to recommend optimal image acquisition and analysis methods to enable WB-MRI as an endpoint in NF clinical trials.

METHODS

A systematic process was used to review all published data about WB-MRI in NF syndromes to assess diagnostic accuracy, feasibility and reproducibility, and data about specific techniques for assessment of tumor burden, characterization of neoplasms, and response to therapy.

RESULTS

WB-MRI at 1.5T or 3.0T is feasible for image acquisition. Short tau inversion recovery (STIR) sequence is used in all investigations to date, suggesting consensus about the utility of this sequence for detection of WB tumor burden in people with NF. There are insufficient data to support a consensus statement about the optimal imaging planes (axial vs coronal) or 2D vs 3D approaches. Functional imaging, although used in some NF studies, has not been systematically applied or evaluated. There are no comparative studies between regional vs WB-MRI or evaluations of WB-MRI reproducibility.

CONCLUSIONS

WB-MRI is feasible for identifying tumors using both 1.5T and 3.0T systems. The STIR sequence is a core sequence. Additional investigation is needed to define the optimal approach for volumetric analysis, the reproducibility of WB-MRI in NF, and the diagnostic performance of WB-MRI vs regional MRI.

QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials

Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, -35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.

Development of a group and family-based cognitive behavioural therapy program for youth at risk for psychosis

OBJECTIVE

The onset of psychosis typically occurs during adolescence or early adulthood and can have a detrimental impact on social and cognitive development. Cognitive behavioural therapy (CBT) shows promise in reducing the risk of psychosis. Teaching families to apply CBT with their offspring may bolster therapeutic gains made in time-limited treatment. We developed a comprehensive group-and-family-based CBT (GF-CBT) program that aims to facilitate psychosocial recovery, decrease symptoms and prevent transition to psychosis in youth at risk. GF-CBT is grounded in ecological systems and cognitive theories, resilience models and research on information processing in delusions. The theoretical rationale and description of GF-CBT are presented together with a pilot study that evaluated the program's feasibility and explored participants' outcomes.

METHODS

Youth ages 16-21 at risk for psychosis and their families participated in an open trial with pre, post and 3-month follow-up assessments conducted by an independent evaluator. The Comprehensive Assessment of At-Risk Mental States was the primary clinical outcome measure.

RESULTS

All enrolled participants (n = 6) completed GF-CBT and all remitted from at-risk mental state (ARMS). As a group participants showed statistically significant decreases in attenuated psychotic symptoms, negative symptoms, depression, cognitive biases and improvements in functioning. Family members showed significant improvements in use of CBT skills, enhanced communication with their offspring, and greater confidence in their ability to help. Gains were maintained at follow-up.

CONCLUSIONS

GF-CBT may delay or prevent transition to psychosis in youth at risk, and potentially facilitate recovery from ARMS. More rigorous, controlled research is needed to further evaluate this program.

Collagen fibers mediate MRI-detected water diffusion and anisotropy in breast cancers

Collagen 1 (Col1) fibers play an important role in tumor interstitial macromolecular transport and cancer cell dissemination. Our goal was to understand the influence of Col1 fibers on water diffusion, and to examine the potential of using noninvasive diffusion tensor imaging (DTI) to indirectly detect Col1 fibers in breast lesions. We previously observed, in human MDA-MB-231 breast cancer xenografts engineered to fluoresce under hypoxia, relatively low amounts of Col1 fibers in fluorescent hypoxic regions. These xenograft tumors together with human breast cancer samples were used here to investigate the relationship between Col1 fibers, water diffusion and anisotropy, and hypoxia. Hypoxic low Col1 fiber containing regions showed decreased apparent diffusion coefficient (ADC) and fractional anisotropy (FA) compared to normoxic high Col1 fiber containing regions. Necrotic high Col1 fiber containing regions showed increased ADC with decreased FA values compared to normoxic viable high Col1 fiber regions that had increased ADC with increased FA values. A good agreement of ADC and FA patterns was observed between in vivo and ex vivo images. In human breast cancer specimens, ADC and FA decreased in low Col1 containing regions. Our data suggest that a decrease in ADC and FA values observed within a lesion could predict hypoxia, and a pattern of high ADC with low FA values could predict necrosis. Collectively the data identify the role of Col1 fibers in directed water movement and support expanding the evaluation of DTI parameters as surrogates for Col1 fiber patterns associated with specific tumor microenvironments as companion diagnostics and for staging.

Barriers to transition in young adults with neurogenic bladder

INTRODUCTION

'Transition' not only involves finding an adult healthcare provider, but also includes the process of developing the patient's ability to care for him/herself. Recent literature states that 40% of young adults with special healthcare needs are receiving the tools needed for transition. Pediatric urologists treating patients with complex anomalies, such as spina bifida, often anticipate poor outcomes for patients who are ill equipped for transition to adult care. The goal of this study was to identify potential barriers for young adults with neurogenic bladder when transitioning to independent care.

STUDY DESIGN

A prospective IRB-approved study was performed on all patients with neurogenic bladder referred to the transitional urology clinic. Reasons for missed appointments were tracked, and all patients were asked to complete the Transition Readiness Assessment Questionnaire (TRAQ) in private prior to an appointment. The TRAQ responses are scaled 1-5, with higher numbers corresponding to higher transition readiness of each individual skill. The mean score for each question was calculated across all patients, and the mean TRAQ score was calculated across all questions for each patient. To assess if certain subgroups were more prepared for transition, mean scores were compared between sexes, patients aged <19 and ≥19 years old, and between ambulatory and full-time wheelchair users with unpaired t-tests.

RESULTS

A total of 73% (58/79) of patients referred to the transitional clinic came to their appointment. The most common reason for missed clinic appointments was related to health insurance coverage (47%). A total of 42 patients completed the TRAQ at a mean age of 19.5 years old; 90% (38/42) had spina bifida. Females, ambulatory patients, and those ≥19 years old had higher overall mean TRAQ scores, but these differences were not statistically significant. The highest TRAQ scores were related to taking and ordering medications, utilization of medical supplies, communication with healthcare providers, and assisting with household duties. The majority of the patients indicated 'I am learning to do this'. The lowest scores were in response to questions about health insurance coverage, payments for medications or medical equipment, financial help, and utilization of community services. Most patients responded 'I do not know how but I want to learn'.

CONCLUSIONS

Young adults with neurogenic bladder needed the most guidance during transition to independent care, with management of health insurance and finances. Based on these findings, dedicated social work and nurse visits have been included into the transition process.

Pyomelanin-producing Pseudomonas aeruginosa selected during chronic infections have a large chromosomal deletion which confers resistance to pyocins

When bacterial lineages make the transition from free-living to permanent association with hosts, they can undergo massive gene losses, for which the selective forces within host tissues are unknown. We identified here melanogenic clinical isolates of Pseudomonas aeruginosa with large chromosomal deletions (66 to 270 kbp) and characterized them to investigate how they were selected. When compared with their wild-type parents, melanogenic mutants (i) exhibited a lower fitness in growth conditions found in human tissues, such as hyperosmolarity and presence of aminoglycoside antibiotics, (ii) narrowed their metabolic spectrum with a growth disadvantage with particular carbon sources, including aromatic amino acids and acyclic terpenes, suggesting a reduction of metabolic flexibility. Despite an impaired fitness in rich media, melanogenic mutants can inhibit their wild-type parents and compete with them in coculture. Surprisingly, melanogenic mutants became highly resistant to two intraspecific toxins, the S-pyocins AP41 and S1. Our results suggest that pyocins produced within a population of infecting P. aeruginosa may have selected for bacterial mutants that underwent massive gene losses and that were adapted to the life in diverse bacterial communities in the human host. Intraspecific interactions may therefore be an important factor driving the continuing evolution of pathogens during host infections.

Multiparametric and Multimodality Functional Radiological Imaging for Breast Cancer Diagnosis and Early Treatment Response Assessment

Breast cancer is the second leading cause of cancer death among US women, and the chance of a woman developing breast cancer sometime during her lifetime is one in eight. Early detection and diagnosis to allow appropriate locoregional and systemic treatment are key to improve the odds of surviving its diagnosis. Emerging data also suggest that different breast cancer subtypes (phenotypes) may respond differently to available adjuvant therapies. There is a growing understanding that not all patients benefit equally from systemic therapies, and therapeutic approaches are being increasingly personalized based on predictive biomarkers of clinical benefit. Optimal use of established and novel radiological imaging methods, such as magnetic resonance imaging and positron emission tomography, which have different biophysical mechanisms can simultaneously identify key functional parameters. These methods provide unique multiparametric radiological signatures of breast cancer, that will improve the accuracy of early diagnosis, help select appropriate therapies for early stage disease, and allow early assessment of therapeutic benefit.

Radiomics: a new application from established techniques

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.

Efficacy and Biomarker Study of Bevacizumab for Hearing Loss Resulting From Neurofibromatosis Type 2-Associated Vestibular Schwannomas

PURPOSE

Neurofibromatosis type 2 (NF2) is a tumor predisposition syndrome characterized by bilateral vestibular schwannomas (VSs) resulting in deafness and brainstem compression. This study evaluated efficacy and biomarkers of bevacizumab activity for NF2-associated progressive and symptomatic VSs.

PATIENTS AND METHODS

Bevacizumab 7.5 mg/kg was administered every 3 weeks for 46 weeks, followed by 24 weeks of surveillance after treatment with the drug. The primary end point was hearing response defined by word recognition score (WRS). Secondary end points included toxicity, tolerability, imaging response using volumetric magnetic resonance imaging analysis, durability of response, and imaging and blood biomarkers.

RESULTS

Fourteen patients (estimated to yield > 90% power to detect an alternative response rate of 50% at alpha level of 0.05) with NF2, with a median age of 30 years (range, 14 to 79 years) and progressive hearing loss in the target ear (median baseline WRS, 60%; range 13% to 82%), were enrolled. The primary end point, confirmed hearing response (improvement maintained ≥ 3 months), occurred in five (36%) of 14 patients (95% CI, 13% to 65%; P < .001). Eight (57%) of 14 patients had transient hearing improvement above the 95% CI for WRS. No patients experienced hearing decline. Radiographic response was seen in six (43%) of 14 target VSs. Three grade 3 adverse events, hypertension (n = 2) and immune-mediated thrombocytopenic purpura (n = 1), were possibly related to bevacizumab. Bevacizumab treatment was associated with decreased free vascular endothelial growth factor (not bound to bevacizumab) and increased placental growth factor in plasma. Hearing responses were inversely associated with baseline plasma hepatocyte growth factor (P = .019). Imaging responses were associated with high baseline tumor vessel permeability and elevated blood levels of vascular endothelial growth factor D and stromal cell-derived factor 1α (P = .037 and .025, respectively).

CONCLUSION

Bevacizumab treatment resulted in durable hearing response in 36% of patients with NF2 and confirmed progressive VS-associated hearing loss. Imaging and plasma biomarkers showed promising associations with response that should be validated in larger studies.

Diffusion-weighted MR imaging for characterizing musculoskeletal lesions

Diffusion-weighted (DW) imaging is a functional magnetic resonance (MR) imaging technique that can readily be incorporated into a routine non-contrast material-enhanced MR imaging protocol with little additional scanning time. DW imaging is based on changes in the Brownian motion of water molecules caused by tissue microstructure. The apparent diffusion coefficient (ADC) is a quantitative measure of Brownian movement: Low ADC values typically reflect highly cellular microenvironments in which diffusion is restricted by the presence of cell membranes, whereas acellular regions allow free diffusion and result in elevated ADC values. Thus, with ADC mapping, one may derive useful quantitative information regarding the cellularity of a musculoskeletal lesion using a nonenhanced technique. The role of localized DW imaging in differentiating malignant from benign osseous and soft-tissue lesions is still evolving; when carefully applied, however, this modality has proved helpful in a subset of tumor types, such as nonmyxoid soft-tissue tumors. Studies of the use of DW imaging in assessing the treatment response of both osseous and soft-tissue tumors have shown that higher ADC values correlate with better response to cytotoxic therapy. Successful application of DW imaging in the evaluation of musculoskeletal lesions requires familiarity with potential diagnostic pitfalls that stem from technical artifacts and confounding factors unrelated to lesion cellularity. Further investigation is needed to evaluate the impact of DW imaging-ADC mapping on management and outcome in patients with musculoskeletal lesions.

Non-absorbable sutures are associated with lower recurrence rates in laparoscopic percutaneous inguinal hernia ligation

INTRODUCTION

Laparoscopic hernia repair with percutaneous ligation of the patent processes vaginalis is a minimally invasive alternative to open inguinal herniorrhaphy in children. With the camera port concealed at the umbilicus, this technique offers an excellent cosmetic result. It is also faster than the traditional laparoscopic repair with no differences in complication rates or hospital stay. The goal of this study was to describe a series of consecutive patients, emphasizing the impact of suture materials (absorbable vs. non-absorbable) on hernia recurrences.

METHODS

A retrospective review was performed of consecutive transperitoneal laparoscopic subcutaneous ligations of a symptomatic hernia and/or communicating hydrocele by 4 surgeons. Patients > Tanner 2 or with prior hernia repair were excluded. The success of the procedure and number of sutures used was compared between cases performed with absorbable vs. non-absorbable suture. Risk factors for surgical failure (age, weight, number of sutures used, suture type) were assessed with logistic regression.

RESULTS

94 patients underwent laparoscopic percutaneous hernia ligation at a mean age of 4.9 years. Outcomes in 85 (90%) patients with 97 hernia repairs at a mean of 8 months after surgery revealed 26% polyglactin vs 4% polyester recurrences (p = 0.004) which occurred at mean of 3.6 months after surgery, Table 1. Repairs performed with non-absorbable suture required only 1 suture more often than those performed with absorbable suture (76% vs 60%, p = 0.163). Logistic regression revealed suture type was an independent predictor for failure (p = 0.017). Weight (p = 0.249), age (p = 0.055), and number of sutures (p = 0.469) were not significantly associated with recurrent hernia.

DISCUSSION

Our review of consecutive hernia repairs using the single port percutaneous ligation revealed a significantly higher recurrent hernia rate with absorbable (26%) versus non-absorbable (4%) suture. This finding remained significant in a logistic regression model irregardless of number of sutures placed, age, and weight. Though the authors acknowledge the drawback of the potential for learning curve to confound our data, we still feel these findings are clinically important as this analysis of outcomes has changed our surgical practice as now all providers involved perform this procedure with exclusively non-absorbable suture. We thus suggest that surgeons who perform this technique, especially those newly adopting it, use non-absorbable suture for optimal patient outcomes.

CONCLUSIONS

Recurrent hernia after laparoscopic percutaneous hernia ligation was significantly lower in repairs performed with non-absorbable suture. Based on this data, we recommend the use of non-absorbable suture during laparoscopic ligation of inguinal hernias in children.