Optimal Neoadjuvant Strategies for Locally Advanced Rectal Cancer by Risk Assessment and Tumor Location.

Neoadjuvant therapy is standard of care for locally advanced rectal cancer (LARC). Advancements in multimodality therapy options and sequencing of radiation therapy (RT), surgery, and chemotherapy make decision-making challenging. Traditional treatment of patients with LARC involves neoadjuvant chemoradiation followed by total mesorectal excision and consideration of adjuvant chemotherapy. Advancement in RT has led to trials offering both short-course and long-course RT with good long-term clinical outcomes. Intensification of therapy in high-risk patients has led to studies of total neoadjuvant therapy with chemotherapy and chemoradiation, now standard management for most LARC. De-escalation of therapy in patients with favorable prognosis has led to several considerations, including non-total mesorectal excision management or neoadjuvant chemotherapy only. Several considerations of patient and disease factors can help inform the optimal chemotherapy regimens in different sequencing of neoadjuvant strategies. Finally, novel biomarkers, such as microsatellite instability, has led to utilization of novel therapies, including neoadjuvant immunotherapy, with substantial response. This review attempts to frame the rapidly growing data in LARC in context of disease and patient risk factors, to inform optimal, personalized treatment of patients with LARC.

Treating the SARS-CoV-2-positive patient with cancer: A proposal for a pragmatic and transparent ethical process.

The treatment of patients with cancer who test positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses unique challenges. In this commentary, the authors describe the ethical rationale and implementation details for the creation of a novel, multidisciplinary treatment prioritization committee, including physicians, frontline staff, an ethicist, and an infectious disease expert. Organizational obligations to health care workers also are discussed. The treatment prioritization committee sets a threshold of acceptable harm to patients from decreased cancer control that is justified to reduce risk to staff. The creation of an ethical, consistent, and transparent decision-making process involving such frontline stakeholders is essential as departments across the country are faced with decisions regarding the treatment of SARS-CoV-2-positive patients with cancer.

Natural history, clinical course and predictors of interval time from initial diagnosis to development of subsequent NSCLC brain metastases.

PURPOSE: Non-small cell lung cancer (NSCLC) brain metastases are associated with substantial morbidity and mortality. During recent years, accompanying dramatic improvements in systemic disease control, NSCLC brain metastases have emerged as an increasingly relevant clinical problem. However, optimal surveillance practices remain poorly defined. This purpose of this study was to further characterize the natural history, clinical course and risk factors associated with earlier development of subsequent NSCLC brain metastases to better inform clinical practice and help guide survivorship care. METHODS: We retrospectively reviewed all institutional NSCLC brain metastasis cases treated with radiotherapy between 1997 and 2015. Exclusion criteria included presence of brain metastases at initial NSCLC diagnosis and incomplete staging information. Interval time to brain metastases and subsequent survival were characterized using Kaplan-Meier and multivariate Cox regression analyses. RESULTS: Among 105 patients within this cohort, median interval time to development of brain metastases was 16 months. Median interval times were 29, 19, 16 and 13 months for Stage I-IV patients, respectively (P = 0.016). Additional independent predictors for earlier development of NSCLC brain metastases included non-adenocarcinomatous histopathology (HR 3.036, P < 0.001), no prior surgical resection (HR 1.609, P = 0.036) and no prior systemic therapy (HR 3.560, P = 0.004). Median survival following intracranial progression was 16 months. Delayed development of brain metastases was associated with better prognosis (HR 0.970, P < 0.001) but not survival following intracranial disease onset. CONCLUSIONS: Collectively, our results provide valuable insights into the natural history of NSCLC brain metastases. NSCLC stage, histology, prior surgical resection and prior systemic therapy emerged as independent predictors for interval time to brain metastases.

Obesity and survival in the neoadjuvant breast cancer setting: role of tumor subtype in an ethnically diverse population.

BACKGROUND: Obesity may negatively affect survival in breast cancer (BC), but studies are conflicting, and associations may vary by tumor subtypes and race/ethnicity groups. METHODS: In a retrospective review, we identified 273 women with invasive BC administered Adriamycin/Taxane-based neoadjuvant chemotherapy from 2004 to 2016 with body mass index (BMI) data at diagnosis. Obesity was defined as BMI ≥30. Associations between obesity and event-free survival (EFS), using STEEP events, and overall survival (OS), using all-cause mortality, were assessed overall and stratified by tumor subtype [[Hormone Receptor Positive (HR+)/HER2-, HER2+, and Triple-Negative Breast Cancer (TNBC])] in our diverse population. RESULTS: Median follow-up was 32.6 months (range 5.7-137.8 months). Overall, obesity was associated with worse EFS (HR 1.71, 95% CI 1.03-2.84, p = 0.04) and a trend towards worse OS (p = 0.13). In HR+/HER2- disease (n = 135), there was an interaction between obesity and hormonal therapy with respect to OS but not EFS. In those receiving tamoxifen (n = 33), obesity was associated with worse OS (HR 9.27, 95% CI 0.96-89.3, p = 0.05). In those receiving an aromatase inhibitor (n = 89), there was no association between obesity and OS. In TNBC (n = 44), obesity was associated with worse EFS (HR 2.62, 95% CI 1.03-6.66, p = 0.04) and a trend towards worse OS (p = 0.06). In HER2+ disease (n = 94), obesity was associated with a trend towards worse EFS (HR 3.37, 95% CI 0.97-11.72, p = 0.06) but not OS. Race/ethnicity was not associated with survival in any subtype, and there were no interactions with obesity on survival. CONCLUSIONS: Obesity may negatively impact survival, with differences among tumor subtypes.

Breast cancer subtype and stage are prognostic of time from breast cancer diagnosis to brain metastasis development.

Breast cancer brain metastasis (BCBM) is associated with high morbidity and mortality. Patients with breast cancer risk factors associated with rapid development of BCBM could potentially benefit from early brain metastasis screening. We retrospectively reviewed all BCBM patients treated with brain radiotherapy at our institution from 1997 to 2015. Interval time to BCBM was defined as date of pathologic breast cancer diagnosis to date of radiographic evidence of brain metastasis. Patients were stratified by breast cancer molecular subtype and stage at diagnosis. Kaplan Meier analysis was conducted on time to development of BCBM. Breast cancer risk factors were correlated with time to BCBM on Cox proportion hazard analysis. The study cohort comprised 121 BCBM patients, with median interval time to BCBM of 46 months. Times to BCBM for Her2+/2HR+, Her2+, Her2-/HR+, and triple-negative (TNBC) subtypes were 70, 44, 42, and 28 months respectively (p = 0.002). Time to BCBM for stages I, II, III, and IV were 70, 54, 29, and 24 months, respectively (p = 0.000). BCBM patients were further stratified by both molecular subtype (TNBC vs. non-TNBC) and stage (I, II vs. III, IV). Median times to BCBM for non-TNBC/stage I-II, TNBC/stage I-II, non-TNBC stage III-IV, and TNBC/stage III-IV were 68, 47, 29, and 6 months respectively (p = 0.000). Subtype and stage were associated with shorter time to BCBM on multivariate analysis. Subtype and initial stage are independently correlated with decreased time to development of BCBM. Patients with advanced high stage and triple negative breast cancer develop brain metastases significantly earlier.

Lymphovascular invasion is an independent predictor of survival in breast cancer after neoadjuvant chemotherapy.

Various prognostic indicators have been investigated in neoadjuvant chemotherapy (NAC)-treated invasive breast cancer (BC). Our study examines if lymphovascular invasion (LVI) is an independent predictor of survival in women receiving NAC. We performed a retrospective analysis in 166 women with operable invasive BC who underwent adriamycin- and taxane-based NAC between 2000 and 2013. The presence of LVI was noted in breast excisions following NAC. Associations between progression-free and overall survival and LVI and other clinicopathologic variables were assessed. Median follow-up was 31 months (range 1.4-153 months) with a total of 56 events and 24 deaths from any cause. LVI was found in 74 of 166 patients (45 %). In univariate analysis, the presence of LVI was associated with worse progression-free survival (HR 3.37, 95 % CI 1.87-6.06, p < 0.01) and overall survival (HR 4.35, 95 % CI 1.61-11.79, p < 0.01). In multivariate models adjusting for breast cancer subtype, LVI was significantly associated with a decrease in progression-free survival (HR 3.76, 95 % CI 2.07-6.83, p < 0.01) and overall survival (HR 5.70, 95 % CI 2.08-15.64, p < 0.01). When stratified by subtype, those with hormone receptor or HER2-positive BCs with no LVI had the most favorable progression-free and overall survival. Those with both LVI and triple-negative BC had the worst progression-free and overall survival. LVI is an important prognostic marker and is associated with worse clinical outcome in breast cancer patients receiving NAC.

Association of Sarcopenia With Toxicity-Related Discontinuation of Adjuvant Endocrine Therapy in Women With Early-Stage Hormone Receptor-Positive Breast Cancer.

PURPOSE: Sarcopenia, an age-related decline in muscle mass and physical function, is associated with increased toxicity and worse outcomes in women with breast cancer (BC). Sarcopenia may contribute to toxicity-related early discontinuation of adjuvant endocrine  therapy (aET) in women with hormone receptor-positive (HR+) BC but remains poorly characterized. METHODS AND MATERIALS: This multicenter, retrospective cohort study included consecutive women with stage 0-II HR+ BC who received breast conserving therapy (lumpectomy and radiation therapy) and aET from 2011 to 2017 with a 5-year follow-up. Skeletal muscle index (SMI, cm2/m2) was analyzed using a deep learning model on routine cross-sectional radiation simulation imaging; sarcopenia was dichotomized according to previously validated reports. The primary endpoint was toxicity-related aET discontinuation; logistic regression analysis evaluated associations between SMI/sarcopenia and aET discontinuation. Cox regression analysis evaluated associations with time to aET toxicity, ipsilateral breast tumor recurrence (IBTR), and disease-free survival (DFS). RESULTS: A total of 305 women (median follow-up, 89 months) were included with a median age of 67 years and early-stage BC (12% stage 0, 65% stage I). A total of 60 (20%) women experienced toxicity-related aET discontinuation. Sarcopenia was associated with toxicity-related early discontinuation of aET (odds ratio, 2.18; P = .036) and shorter time to aET toxicity (hazard ratio [HR], 1.62; P = .031). SMI or sarcopenia were not independently associated with IBTR or DFS; toxicity-related aET discontinuation was associated with worse IBTR (HR, 9.47; P = .002) and worse DFS (HR, 4.53; P = .001). CONCLUSIONS: Among women with early-stage HR+ BC who receive adjuvant radiation therapy and hormone therapy, sarcopenia is associated with toxicity-related early discontinuation of aET. Further studies should validate these findings in women who did not receive adjuvant radiation therapy. These high-risk patients may be candidates for aggressive symptom management and/or alternative treatment strategies to improve outcomes.

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.

Importance: Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use. Objective: To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes. Design, Setting, and Participants: For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023. Exposure: C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC. Main Outcomes and Measures: Overall survival and treatment toxicity outcomes of HNSCC. Results: The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia. Conclusions and Relevance: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.

Purpose: Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes. Materials and Methods: 899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression. Results: DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r ß 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis. Conclusion: We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC. SUMMARY STATEMENT: In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.

Patient-Reported Outcomes, Tumor Markers, and Survival Outcomes in Advanced GI Cancer.

Importance: Patient-reported outcomes (PROs), such as quality of life (QOL) and symptoms, are often associated with clinical outcomes in patients with cancer. In practice, oncologists use serum tumor markers (TMs) (ie, carcinoembryonic antigen [CEA] and carbohydrate antigen 19-9 [CA 19-9]) and imaging to monitor clinical outcomes in patients with gastrointestinal cancer. Objective: To examine associations of 1-month changes in PROs and TMs with treatment response and survival among patients with gastrointestinal cancer. Design, Setting, and Participants: This cohort study enrolled patients at Massachusetts General Hospital Cancer Center with at least 1 month follow-up from May 2019 to December 2020. Included patients were beginning first-line systemic therapy, aged 18 years or older, and had been diagnosed with metastatic pancreaticobiliary, colorectal, or gastroesophageal cancer. Data analyses took place from January 2021 to January 2022. Intervention: PROs were collected, including QOL (Functional Assessment of Cancer Therapy General [FACT-G]), physical symptoms (Edmonton Symptom Assessment System [ESAS]), and psychological symptoms (Patient Health Questionnaire-4 [PHQ4] total, PHQ4-depression, and PHQ4-anxiety), as well as TMs (CEA and CA 19-9), at the time of chemotherapy initiation and 1 month later. Main Outcomes and Measures: Associations of 1-month changes in PROs and TMs with treatment response (clinical benefit vs disease progression) at first scan, progression-free survival (PFS), and overall survival (OS), adjusted for baseline values using regression models. Results: This study included 159 patients, with 134 patients (84.3%) evaluable for analysis. Patients had a median (range) age of 64.0 (28.0-84.0) years and 86 (64.2%) were male. One-month PRO changes (FACT-G: OR, 1.07; 95% CI, 1.03-1.11; P = .001; ESAS-total: OR, 0.97; 95% CI, 0.94-1.00; P = .02; ESAS-physical: OR, 0.96; 95% CI, 0.92-1.00; P = .03; PHQ4-depression: OR, 0.67; 95% CI, 0.49-0.92; P = .01) were significantly associated with treatment response, but PHQ4-total or TMs were not. Changes in FACT-G (HR, 0.97; 95% CI, 0.95-0.99; P = .003), ESAS-total (HR, 1.03; 95% CI, 1.01-1.05; P = .004), ESAS-physical (HR, 1.03; 95% CI, 1.00-1.05; P = .02), PHQ4-depression (HR, 1.22; 95% CI, 1.01-1.48; P = .04), and CEA (HR, 1.00; 95% CI, 1.001-1.004; P = .001) were associated with PFS, but changes in PHQ4-total or TMs were not. Changes in ESAS-total (HR, 1.03, 95% CI, 1.01-1.06; P = .006) and ESAS-physical (HR, 1.04, 95% CI, 1.01-1.06; P = .015) were associated with OS, but changes in TMs were not associated with OS. Conclusions and Relevance: These findings suggest that 1-month changes in PROs can be associated with treatment response and survival in patients with advanced gastrointestinal cancer. Notably, 1-month changes in TMs were not consistently associated with these outcomes. These findings highlight the potential for monitoring early changes in PROs to associate with clinical outcomes while underscoring the need to address the QOL and symptom concerns of patients with advanced cancer.

Automated temporalis muscle quantification and growth charts for children through adulthood.

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Novel Clinical Trial Designs in Neuro-Oncology.

Scientific and technologic advances have led to a boon of candidate therapeutics for patients with malignancies of the central nervous system. The path from drug development to clinical use has generally followed a regimented order of sequential clinical trial phases. The recent increase in novel therapies, however, has strained the regulatory process and unearthed limitations of the current system, including significant cost, prolonged development time, and difficulties in testing therapies for rarer tumors. Novel clinical trial designs have emerged to increase efficiencies in clinical trial conduct to better evaluate and bring impactful drugs to patients in a timely manner. In order to better capture meaningful benefits for brain tumor patients, new endpoints to complement or replace traditional endpoints are also an increasingly important consideration. This review will explore the current challenges in the current clinical trial landscape and discuss novel clinical trial concepts, including consideration of limitations and risks of novel trial designs, within the context of neuro-oncology.

Healthcare Disparities and Outcomes of Cancer Patients in a Community Setting from a COVID-19 Epicenter.

There have been numerous studies demonstrating how cancer patients are at an increased risk of mortality. Within New York City, our community hospital emerged as an epicenter of the first wave of the pandemic in the spring of 2020 and serves a unique population that is predominately uninsured, of a lower income, and racially/ethnically diverse. In this single institution retrospective study, the authors seek to investigate COVID-19 diagnosis, severity and mortality in patients with an active cancer diagnosis. Demographic, clinical characteristics, treatment, SARS-CoV-2 laboratory results, and outcomes were evaluated. In our community hospital during the first wave of the COVID-19 pandemic in the United States, patients with active cancer diagnosis appear to be at increased risk for mortality (30%) and severe events (50%) due to the SARS-CoV-2 infection compared to the general population. A higher proportion of active cancer patients with Medicaid insurance, Hispanic ethnicity, other race, and male sex had complications and death from COVID-19 infection. The pandemic has highlighted the health inequities that exist in vulnerable patient populations and underserved communities such as ours.

Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.

Importance: Despite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care. Objective: To systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions. Evidence Review: In this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed. Findings: Literature search yielded 19 737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%). Conclusions and Relevance: This systematic review found that despite the large number of medical machine learning-based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.

Long-term outcomes and late toxicity of adult medulloblastoma treated with combined modality therapy: A contemporary single-institution experience.

BACKGROUND: Medulloblastoma (MB) is a rare central nervous system malignancy of adults, with limited contemporary studies to define treatment guidelines and expected late toxicity. METHODS: A single-center, retrospective study was conducted of patients age ≥18 years from 1997-2019 with MB and who were treated with postoperative radiotherapy. Late toxicity was defined as a minimum of 18 months from diagnosis. Overall survival (OS) and progression-free survival (PFS) were characterized using Kaplan-Meier and Cox regression analyses. RESULTS: Fifty-nine patients met criteria, with median age of 25 years (range 18-62 y) and median follow-up of 6.5 years (range 0.7-23.1 y). At diagnosis, 68% were standard-risk, 88% Chang M0, and 22% with anaplastic histology. Gross total resection was achieved in 75%; median craniospinal irradiation dose was 30.6 Gy (relative biological effectiveness [RBE]), median total dose was 54.0 Gy (RBE), 80% received proton radiotherapy; 81% received chemotherapy. 5 year PFS and OS were 86.5% and 95.8%, respectively; 10 year PFS and OS were 83.9% and 90.7%, respectively. Anaplastic histology was associated with worse PFS (P = .04). Among eight recurrences, 25% presented after 5 years. Most common grade ≥2 late toxicities were anxiety/depressive symptoms (30%), motor dysfunction (25%), and ototoxicity (22%). Higher posterior fossa radiation dose was associated with increased risk of late toxicity, including worse cognitive dysfunction (P = .05). CONCLUSIONS: Adults with MB have favorable survival outcomes, but late failures and toxicity are not uncommon. Better understanding of prognostic factors, possibly from molecular subtyping, may help to define more personalized treatments for patients with high risk of recurrence and long-term treatment sequelae.

Characterization of an Iodinated Rectal Spacer for Prostate Photon and Proton Radiation Therapy.

PURPOSE: Conventional rectal spacers (nonI-SPs) are low-contrast on computed tomography (CT), often necessitating magnetic resonance imaging for accurate delineation. A new formulation of spacers (I-SPs) incorporates iodine to improve radiopacity and CT visualization. We characterized placement, stability, and plan quality of I-SPs compared to nonI-SPs. METHODS AND MATERIALS: Patients with intact prostate cancer (n = 50) treated with I-SPs and photons were compared to randomly selected patients (n = 50) with nonI-SPs (photon or proton therapy). The I-SP was contoured on the planning CT and cone beam CTs at 3 timepoints: first, middle, and final treatment (n = 200 scans). I-SPs Hounsfield units (HU), volume, surface area (SA), centroid position relative to prostate centroid, and distance between prostate/rectum centroids were compared on the planning CTs between each cohort. I-SP changes were evaluated on cone beam CTs over courses of treatment. Dosimetric evaluations of plan quality and robustness were performed. I-SP was tested in a phantom to characterize its relative linear stopping power for protons. RESULTS: I-SPs yielded a distinct visible contrast on planning CTs compared to nonI-SPs (HU 138 vs 12, P < .001), allowing delineation on CT alone. The delineated volume and SA of I-SPs were smaller than nonI-SPs (volume 8.9 vs 10.6 mL, P < .001; SA 28 vs 35 cm2, P < .001), yet relative spacer position and prostate-rectal separation were similar (P = .79). No significant change in HU, volume, SA, or relative position of the I-SPs hydrogel occurred over courses of treatment (all P > .1). Dosimetric analysis concluded there were no significant changes in plan quality or robustness for I-SPs compared to nonI-SPs. The I-SP relative linear stopping power was 1.018, necessitating HU override for proton planning. CONCLUSIONS: I-SPs provide a manifest CT contrast, allowing for delineation on planning CT alone with no magnetic resonance imaging necessary. I-SPs radiopacity, size, and relative position remained stable over courses of treatment from 28 to 44 fractions. No changes in plan quality or robustness were seen comparing I-SPs and nonI-SPs.

Fractionated Proton Radiation Therapy and Hearing Preservation for Vestibular Schwannoma: Preliminary Analysis of a Prospective Phase 2 Clinical Trial.

BACKGROUND: Local management for vestibular schwannoma (VS) is associated with excellent local control with focus on preserving long-term serviceable hearing. Fractionated proton radiation therapy (FPRT) may be associated with greater hearing preservation because of unique dosimetric properties of proton radiotherapy. OBJECTIVE: To investigate hearing preservation rates of FPRT in adults with VS and secondarily assess local control and treatment-related toxicity. METHODS: A prospective, single-arm, phase 2 clinical trial was conducted of patients with VS from 2010 to 2019. All patients had serviceable hearing at baseline and received FPRT to a total dose of 50.4 to 54 Gy relative biological effectiveness (RBE) over 28 to 30 fractions. Serviceable hearing preservation was defined as a Gardner-Robertson score of 1 to 2, measured by a pure tone average (PTA) of ≤50 dB and a word recognition score (WRS) of ≥50%. RESULTS: Twenty patients had a median follow-up of 4.0 years (range 1.0-5.0 years). Local control at 4 years was 100%. Serviceable hearing preservation at 1 year was 53% (95% CI 29%-76%), and primary end point was not yet reached. Median PTA and median WRS both worsened 1 year after FPRT (P < .0001). WRS plateaued after 6 months, whereas PTA continued to worsen up to 1 year after FPRT. Median cochlea D90 was lower in patients with serviceable hearing at 1 year (40.6 Gy [RBE] vs 46.9 Gy [RBE]), trending toward Wilcoxon rank-sum test statistical significance (P = .0863). Treatment was well-tolerated, with one grade 1 cranial nerve V dysfunction and no grade 2+ cranial nerve dysfunction. CONCLUSION: FPRT for VS did not meet the goal of serviceable hearing preservation. Higher cochlea doses trended to worsening hearing preservation, suggesting that dose to cochlea correlates with hearing preservation independent of treatment modality.

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

BACKGROUND: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts. METHODS: In this observational study, CT images and segmentations were collected from eight internal and external sources from the USA, the Netherlands, Canada, and China, with patients from the Maastro and Harvard-RT1 datasets used for model discovery (segmented by a single expert). Validation consisted of interobserver and intraobserver benchmarking, primary validation, functional validation, and end-user testing on the following datasets: multi-delineation, Harvard-RT1, Harvard-RT2, RTOG-0617, NSCLC-radiogenomics, Lung-PET-CT-Dx, RIDER, and thorax phantom. Primary validation consisted of stepwise testing on increasingly external datasets using measures of overlap including volumetric dice (VD) and surface dice (SD). Functional validation explored dosimetric effect, model failure modes, test-retest stability, and accuracy. End-user testing with eight experts assessed automated segmentations in a simulated clinical setting. FINDINGS: We included 2208 patients imaged between 2001 and 2015, with 787 patients used for model discovery and 1421 for model validation, including 28 patients for end-user testing. Models showed an improvement over the interobserver benchmark (multi-delineation dataset; VD 0·91 [IQR 0·83-0·92], p=0·0062; SD 0·86 [0·71-0·91], p=0·0005), and were within the intraobserver benchmark. For primary validation, AI performance on internal Harvard-RT1 data (segmented by the same expert who segmented the discovery data) was VD 0·83 (IQR 0·76-0·88) and SD 0·79 (0·68-0·88), within the interobserver benchmark. Performance on internal Harvard-RT2 data segmented by other experts was VD 0·70 (0·56-0·80) and SD 0·50 (0·34-0·71). Performance on RTOG-0617 clinical trial data was VD 0·71 (0·60-0·81) and SD 0·47 (0·35-0·59), with similar results on diagnostic radiology datasets NSCLC-radiogenomics and Lung-PET-CT-Dx. Despite these geometric overlap results, models yielded target volumes with equivalent radiation dose coverage to those of experts. We also found non-significant differences between de novo expert and AI-assisted segmentations. AI assistance led to a 65% reduction in segmentation time (5·4 min; p<0·0001) and a 32% reduction in interobserver variability (SD; p=0·013). INTERPRETATION: We present a clinical validation strategy for AI models. We found that in silico geometric segmentation metrics might not correlate with clinical utility of the models. Experts' segmentation style and preference might affect model performance. FUNDING: US National Institutes of Health and EU European Research Council.

A Paradigm Shift in Radiation Oncology Training.

The coronavirus disease 2019 pandemic has been intertwined with the movement for racial justice in the United States and has highlighted and risks aggravating educational and workforce disparities within radiation oncology. We discuss wide-ranging changes within radiation oncology training that are essential to developing and maintaining diversity, including utilization of competency-based educational models that allow for streamlining of training and examinations; responsiveness to the needs of residents and medical students of different gender, racial/ethnic, and socioeconomic groups; and technological integration to increase educational efficiency and decrease barriers.

Improving Diversity and Inclusion in the Post-Coronavirus Disease 2019 Era Through a Radiation Oncology Intensive Shadowing Experience (RISE).

PURPOSE: In response to the coronavirus disease 2019 pandemic, current Association of American Medical Colleges guidelines discourage away rotations, posing significant challenges for attracting students to radiation oncology (RO). This is particularly concerning for medical students underrepresented in medicine (UIM) due to the potential of widening existing disparities in applicant and workforce composition. To proactively address this, we created a Radiation Oncology Intensive Shadowing Experience (RISE) to expose UIM students to the field of RO. METHODS AND MATERIALS: Key stakeholders within the residency program, including both UIM faculty and residents with experience in health disparities and medical education, designed a 1-week virtual RISE intended for fourth year UIM students recruited through established national organizations serving UIM medical students. A 1-week disease-specific curriculum was developed using 4 components: (1) foundational exposure to RO, (2) didactic teaching, (3) mentorship opportunities, and (4) a capstone experience. Mentorship was continuously weaved through the experience by attendings, peer resident mentors, and a UIM resident panel to optimize exposure. RESULTS: RISE was successfully initiated at 2 academic medical centers with 12 UIM students enrolled through August. Anonymized pre- and postclerkship surveys were developed for students, residents, and faculty involved in RISE to evaluate participants' satisfaction, resident and attending time burden, and perceptions of program effectiveness. CONCLUSIONS: We created a unique virtual RO shadowing experience for UIM students to address a critical gap in exposure to RO, heightened by the corona virus disease 2019 pandemic, with the goal of improving diversity, equity, and inclusion in our field.