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BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. METHODS: We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from two institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained three DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with 1) clinical features, 2) DL-MRI features, and 3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model. RESULTS: Of the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, p<0.0001). CONCLUSIONS: DL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.
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BACKGROUND: Serum antibodies to the Merkel oncoprotein (AMERK) are detectable in approximately 50% of patients with Merkel cell carcinoma (MCC) and can be used to monitor for recurrence. The objective of this study was to characterize AMERK levels in patients receiving curative-intent radiation therapy (RT) for MCC and identify associations between AMERK and recurrence. METHODS: This was a retrospective study of patients with MCC who had baseline AMERK measurements before they received curative-intent RT from 2010 to 2020. Event-free survival (EFS) was calculated using the Kaplan-Meier method and Cox regression. The cumulative incidence of MCC-related recurrence (CIMR) was analyzed with death as a competing risk and the Gray test. RESULTS: The authors identified 88 patients who had baseline AMERK measurements, including 52 (59%) with detectable levels. AMERK positivity was associated with younger median age (67.8 vs. 72.0 years; p = .02) and tumor site (p = 0.02), with lower rates for those who had disease in the head/neck region (17.3% vs. 44.4%). EFS (71.3% vs. 60.4%; p = .30) and CIMR (24.4% vs. 39.6%; p = .23) were more favorable in AMERK-positive patients. Two patients had recurrences in the RT field, and both were AMERK-negative at baseline. The median time to AMERK nadir after RT was 11.2 months; and, in a 6-month post-RT landmark analysis, the proportion of patients who were AMERK-positive who became negative or who had levels that decreased by ≥50% were not associated with EFS (87.1% vs. 85.0%; p = .90) or CIMR (12.9% vs. 15.0%; p = .62). CONCLUSIONS: Positive AMERK baseline levels were correlated with younger age at MCC diagnosis and nonhead and neck tumor location, possibly related to the distribution of viral etiology. A specific post-RT AMERK decline correlating with EFS could not be identified.
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Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient's current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.
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Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024.
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Neoplasias Encefálicas , Aprendizaje Profundo , Imagen por Resonancia Magnética , Humanos , Niño , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Imagen por Resonancia Magnética/métodos , Masculino , Adolescente , Preescolar , Estudios Retrospectivos , Femenino , Lactante , Adulto Joven , Glioma/diagnóstico por imagen , Glioma/patología , Interpretación de Imagen Asistida por Computador/métodosRESUMEN
BACKGROUND: Prognosis for patients with high-risk neuroblastoma (HR-NBL) is guarded despite aggressive therapy, and few studies have characterized outcomes after radiotherapy in relation to radiation treatment fields. METHODS: Multi-institutional retrospective cohort of 293 patients with HR-NBL who received autologous stem cell transplant (ASCT) and EBRT between 1997-2021. LRR was defined as recurrence at the primary site or within one nodal echelon beyond disease present at diagnosis. Follow-up was defined from the end of EBRT. Event-free survival (EFS) and OS were analyzed by Kaplan-Meier method. Cumulative incidence of locoregional progression (CILP) was analyzed using competing risks of distant-only relapse and death with Gray's test. RESULTS: Median follow-up was 7.0 years (range: 0.01-22.4). Five-year CILP, EFS, and OS were 11.9 %, 65.2 %, and 77.5 %, respectively. Of the 31 patients with LRR and imaging review, 15 (48.4 %) had in-field recurrences (>12 Gy), 6 (19.4 %) had marginal failures (≤12 Gy), and 10 (32.3 %) had both in-field and marginal recurrences. No patients receiving total body irradiation (12 Gy) experienced marginal-only failures (p = 0.069). On multivariable analyses, MYCN amplification had higher risk of LRR (HR: 2.42, 95 % CI: 1.06-5.50, p = 0.035) and post-consolidation isotretinoin and anti-GD2 antibody therapy (HR: 0.42, 95 % CI: 0.19-0.94, p = 0.035) had lower risk of LRR. CONCLUSIONS: Despite EBRT, LRR remains a contributor to treatment failure in HR-NBL with approximately half of LRRs including a component of marginal failure. Future prospective studies are needed to explore whether radiation fields and doses should be defined based on molecular features such as MYCN amplification, and/or response to chemotherapy.
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Recurrencia Local de Neoplasia , Neuroblastoma , Humanos , Neuroblastoma/radioterapia , Neuroblastoma/mortalidad , Estudios Retrospectivos , Masculino , Femenino , Preescolar , Lactante , Niño , Dosificación Radioterapéutica , AdolescenteRESUMEN
Intrinsic, expansile pontine tumors typically occur in the pediatric population. These tumors characteristically present as diffuse intrinsic pontine glioma (DIPG), which is now considered as diffuse midline glioma (DMG), H3K27-mutated of the pons. DIPG has limited treatment options and a poor prognosis, and the value of tissue diagnosis from an invasive biopsy remains controversial. This study presents the case of a 19-year-old female with clinical and imaging hallmarks of DIPG, who underwent a biopsy of a tumor in the region of the right middle cerebellar peduncle. Her lesional cells were negative for H3K27M alterations and had low-grade histologic features. Next-generation sequencing revealed a frameshift mutation in the NF1 gene as the likely driver mutation. These features suggest a diagnosis of a low-grade glioma associated with NF1 loss of function, with far-reaching consequences regarding both treatment strategy and prognosis. This case provides support for the utility of diagnostic tissue biopsy in cases of suspected DIPG.
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Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.
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Neoplasias Encefálicas , Glioma , Humanos , Niño , Masculino , Femenino , Neoplasias Encefálicas/diagnóstico por imagen , Estudios Retrospectivos , Proteínas Proto-Oncogénicas B-raf/genética , Glioma/diagnóstico , Aprendizaje AutomáticoRESUMEN
PURPOSE: The PNOC001 phase II single-arm trial sought to estimate progression-free survival (PFS) associated with everolimus therapy for progressive/recurrent pediatric low-grade glioma (pLGG) on the basis of phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway activation as measured by phosphorylated-ribosomal protein S6 and to identify prognostic and predictive biomarkers. PATIENTS AND METHODS: Patients, age 3-21 years, with progressive/recurrent pLGG received everolimus orally, 5 mg/m2 once daily. Frequency of driver gene alterations was compared among independent pLGG cohorts of newly diagnosed and progressive/recurrent patients. PFS at 6 months (primary end point) and median PFS (secondary end point) were estimated for association with everolimus therapy. RESULTS: Between 2012 and 2019, 65 subjects with progressive/recurrent pLGG (median age, 9.6 years; range, 3.0-19.9; 46% female) were enrolled, with a median follow-up of 57.5 months. The 6-month PFS was 67.4% (95% CI, 60.0 to 80.0) and median PFS was 11.1 months (95% CI, 7.6 to 19.8). Hypertriglyceridemia was the most common grade ≥3 adverse event. PI3K/AKT/mTOR pathway activation did not correlate with clinical outcomes (6-month PFS, active 68.4% v nonactive 63.3%; median PFS, active 11.2 months v nonactive 11.1 months; P = .80). Rare/novel KIAA1549::BRAF fusion breakpoints were most frequent in supratentorial midline pilocytic astrocytomas, in patients with progressive/recurrent disease, and correlated with poor clinical outcomes (median PFS, rare/novel KIAA1549::BRAF fusion breakpoints 6.1 months v common KIAA1549::BRAF fusion breakpoints 16.7 months; P < .05). Multivariate analysis confirmed their independent risk factor status for disease progression in PNOC001 and other, independent cohorts. Additionally, rare pathogenic germline variants in homologous recombination genes were identified in 6.8% of PNOC001 patients. CONCLUSION: Everolimus is a well-tolerated therapy for progressive/recurrent pLGGs. Rare/novel KIAA1549::BRAF fusion breakpoints may define biomarkers for progressive disease and should be assessed in future clinical trials.
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Everolimus , Glioma , Humanos , Niño , Femenino , Preescolar , Adolescente , Adulto Joven , Adulto , Masculino , Everolimus/efectos adversos , Proteínas Proto-Oncogénicas B-raf/genética , Proteínas Proto-Oncogénicas c-akt , Fosfatidilinositol 3-Quinasas , Glioma/tratamiento farmacológico , Glioma/genética , Serina-Treonina Quinasas TOR/metabolismo , Serina-Treonina Quinasas TOR/uso terapéutico , BiomarcadoresRESUMEN
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.
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Gráficos de Crecimiento , Músculo Temporal , Masculino , Femenino , Humanos , Niño , Músculo Temporal/diagnóstico por imagen , Músculo Temporal/patologíaRESUMEN
Hematologic toxicity is a common side effect of multimodal cancer therapy. Nearly all animal studies investigating the causes of radiotherapy-induced hematologic toxicity use inbred strains with limited genetic diversity and do not reflect the diverse responses observed in humans. We used the population-based Collaborative Cross (CC) mouse resource to investigate the genetic architecture of the acute and persistent immune response after radiation exposure by measuring 22 immune parameters in 1,720 CC mice representing 35 strains. We determined relative acute and persistent radiation resistance scores at the individual strain level considering contributions from all immune parameters. Genome-wide association analysis identified quantitative trait loci associated with baseline and radiation responses. A cross-species radiation resistance score predicted recurrence-free survival in medulloblastoma patients. We present a community resource of immune parameters and genome-wide association analyses before and after radiation exposure for future investigations of the contributions of host genetics on radiosensitivity.
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Introduction: Approximately 1.6 million people in the US identify as transgender, many of whom undergo gender-affirming medical or surgical therapies. While transgender individuals are diagnosed with cancer at similar rates as those who are cisgender, the impacts of radiation therapy on outcomes of gender-affirming care in transgender, nonbinary, and gender-expansive people with cancer are understudied. We report on the experiences and outcomes of transgender and gender-expansive patients receiving radiation therapy for cancer treatment. Methods: This study is a multi-institutional retrospective review of patients evaluated from 2005-2019 identified as transgender or gender-expansive in the medical record and treated with radiation therapy. Results: We identified 23 patients who received radiation to 32 sites, including 12 (38%) to the brain, head, or neck, 8 (25%) to the thorax, and 7 (22%) to the pelvis. Seventeen patients (74%) received gender-affirming hormone therapy and 13 patients (57%) underwent gender-affirming surgery. Four patients had pelvic radiation before or after gender-affirming pelvic surgery, including two trans women who had pelvic radiation after vaginoplasty. Four patients had radiation to the chest or thorax and gender-affirming chest or breast surgery, including two trans men with breast cancer. Two pediatric patients developed hypopituitarism and hypogonadism secondary to radiation therapy and, as adults, changed their hormone replacement therapy to affirm their transgender identities. Discussion: Transgender people with cancer undergo radiation therapy for a wide range of cancers. Understanding their prior gender-affirming medical or surgical treatments and future gender affirmation goals may identify important considerations for their oncologic care.
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BACKGROUND: Central nervous system tumors are the most common solid tumors in childhood. Treatment paradigms for pediatric central nervous system malignancies depend on elements including tumor histology, age of patient, and stage of disease. Radiotherapy is an important modality of treatment for many pediatric central nervous system malignancies. SUMMARY: While radiation contributes to excellent overall survival rates for many patients, radiation also carries significant risks of long-term side effects including neurocognitive decline, hearing loss, growth impairment, neuroendocrine dysfunction, strokes, and secondary malignancies. In recent decades, clinical trials have demonstrated that with better imaging and staging along with more sophisticated radiation planning and treatment set-up verification, smaller treatment volumes can be utilized without decrement in survival. Furthermore, the development of intensity-modulated radiotherapy and proton-beam radiotherapy has greatly improved conformality of radiation. KEY MESSAGES: Recent changes in radiation treatment paradigms have decreased risks of short- and long-term toxicity for common histologies and in different age groups. Future studies will continue to develop novel radiation regimens to improve outcomes in aggressive central nervous system tumors, integrate molecular subtypes to tailor radiation treatment, and decrease radiation-associated toxicity for long-term survivors.
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Neoplasias del Sistema Nervioso Central , Humanos , Niño , Neoplasias del Sistema Nervioso Central/radioterapia , Radioterapia/efectos adversos , Radioterapia/métodosRESUMEN
PURPOSE: Multitargeted tyrosine kinase inhibitors (mTKIs) are increasingly utilized in the treatment of pediatric sarcomas and other solid tumors. It is unknown whether serial treatment with multiple TKIs provides a benefit and which patients are most likely to benefit from mTKI rechallenge. METHODS: We performed a retrospective cohort study of pediatric cancer patients who received serial mTKI therapy off-study between 2007 and 2020 as either monotherapy or combination therapy. We report patient characteristics, clinical outcomes, dosing patterns, and treatment-associated toxicity. RESULTS: The study cohort included 25 patients. The overall prevalence of serial mTKI therapy among all patients treated for sarcoma at our institution was 3.7%, and the response rate to second mTKI was 9%. Median 6-month progression-free survival (PFS) and overall survival (OS) from start of second mTKI were 42.1% (95% CI: 20.4%-62.5%) and 79.1% (95% CI: 57.0%-90.8%), respectively. Patients who had received 4 months or more (n = 11) of therapy with first mTKI had significantly longer PFS versus those who received less than 4 months (n = 11; p = .001). Thirty-three percent of patients discontinued second mTKI due to toxicity. Six (40%) of 15 patients who discontinued the first mTKI due to progression had either a partial response or stable disease on the second mTKI. CONCLUSIONS: We observed a low response rate to mTKI rechallenge. However, we identified patients who had been treated with first mTKI for ≥4 months as more likely to have prolonged stable disease with second mTKI. Several patients had a response or stable disease on the second mTKI despite having progressed on the first mTKI. Though toxicity was common, only a minority of patients discontinued the second mTKI due to toxicity.
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Neoplasias Pulmonares , Sarcoma , Humanos , Niño , Estudios Retrospectivos , Prevalencia , Inhibidores de Proteínas Quinasas/efectos adversos , Sarcoma/tratamiento farmacológico , Neoplasias Pulmonares/tratamiento farmacológicoRESUMEN
Purpose: To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG. Materials and Methods: We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wildtype) via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer and self-supervised approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist, that quantifies the accuracy of model attention around the tumor. Results: A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest macro-average AUC (0.82 [95% CI: 0.70-0.90]) and accuracy (77%) on internal validation, with an AUC improvement of +17.7% and a COMDist improvement of +6.4% versus training from scratch. On external validation, the TransferX model yielded AUC (0.73 [95% CI 0.68-0.88]) and accuracy (75%). Conclusion: Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.
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PURPOSE: Myxopapillary ependymoma (MPE) is a rare, typically slow-growing subtype of spinal ependymomas. There are no standard guidelines for radiotherapy and long-term outcomes after radiation, particularly patterns of relapse, for pediatric and young adult (YA) patients with MPE remain under-characterized. METHODS AND MATERIALS: This is an Institutional Review Board-approved multi-institutional retrospective cohort study of 60 pediatric and YA patients diagnosed with MPE and received radiotherapy between 2000-2020. Clinical and treatment characteristics, and long-term outcomes were recorded. Site(s) of progression was compared to radiation fields. Survival outcomes were analyzed using Kaplan-Meier method. Cumulative incidence of local in-field progression (CILP) after initial radiotherapy was analyzed using Gray's method with out-of-field-only progression as a competing risk. Univariate analyses were performed using Cox proportional hazard's model. RESULTS: The median age at radiation was 14.8 years (range: 7.1-26.5). At time of radiotherapy, 45 (75.0%) and 35 (58.3%) patients had gross residual and multifocal disease, respectively. Forty-eight (80.0%), seven (11.7%) and five (8.3%) patients received involved field radiotherapy, craniospinal irradiation, and whole spine radiation, respectively. Median follow-up from end of radiotherapy was 6.2 years (range: 0.6-21.0). Five-year overall survival, progression-free survival, and CILP were 100%, 60.8% and 4.1%, respectively. Both local recurrences were at sites of gross residual disease. Of the eighteen out-of-field first recurrences after radiotherapy, all were superior to the initial treatment field and nine had intracranial relapse. On univariate analyses, distant-only recurrence before radiation (HR: 4.00, 95% CI: 1.54-10.43, pâ¯=â¯0.005) was significantly associated with shorter time to progression. CONCLUSIONS: While the risk of recurrence within the radiation field is low, pediatric and YA patients with high-risk MPE remain at risk for recurrences in the spine above the radiation field and intracranially after radiotherapy. Future prospective studies are needed to investigate the appropriate radiation field and dose based on the extent of metastases.
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Ependimoma , Neoplasias de la Médula Espinal , Humanos , Niño , Adulto Joven , Adolescente , Adulto , Estudios Retrospectivos , Recurrencia Local de Neoplasia/radioterapia , Neoplasias de la Médula Espinal/radioterapia , RecurrenciaRESUMEN
Purpose: Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms for pediatric tumors are rare, due to limited data availability, and algorithms have yet to demonstrate clinical translation. Methods: We leveraged two datasets from a national brain tumor consortium (n=184) and a pediatric cancer center (n=100) to develop, externally validate, and clinically benchmark deep learning neural networks for pediatric low-grade glioma (pLGG) segmentation using a novel in-domain, stepwise transfer learning approach. The best model [via Dice similarity coefficient (DSC)] was externally validated and subject to randomized, blinded evaluation by three expert clinicians wherein clinicians assessed clinical acceptability of expert- and AI-generated segmentations via 10-point Likert scales and Turing tests. Results: The best AI model utilized in-domain, stepwise transfer learning (median DSC: 0.877 [IQR 0.715-0.914]) versus baseline model (median DSC 0.812 [IQR 0.559-0.888]; p<0.05). On external testing (n=60), the AI model yielded accuracy comparable to inter-expert agreement (median DSC: 0.834 [IQR 0.726-0.901] vs. 0.861 [IQR 0.795-0.905], p=0.13). On clinical benchmarking (n=100 scans, 300 segmentations from 3 experts), the experts rated the AI model higher on average compared to other experts (median Likert rating: 9 [IQR 7-9]) vs. 7 [IQR 7-9], p<0.05 for each). Additionally, the AI segmentations had significantly higher (p<0.05) overall acceptability compared to experts on average (80.2% vs. 65.4%). Experts correctly predicted the origins of AI segmentations in an average of 26.0% of cases. Conclusions: Stepwise transfer learning enabled expert-level, automated pediatric brain tumor auto-segmentation and volumetric measurement with a high level of clinical acceptability. This approach may enable development and translation of AI imaging segmentation algorithms in limited data scenarios.
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Background: Neuroblastoma (NB) is considered an immunologically cold tumor and is usually less responsive to immune checkpoint blockade (ICB). Tumor-associated macrophages (TAMs) are highly infiltrated in NB tumors and promote immune escape and resistance to ICB. Hence therapeutic strategies targeting immunosuppressive TAMs can improve responses to ICB in NB. We recently discovered that spleen tyrosine kinase (Syk) reprograms TAMs toward an immunostimulatory phenotype and enhances T-cell responses in the lung adenocarcinoma model. Here we investigated if Syk is an immune-oncology target in NB and tested whether a novel immunotherapeutic approach utilizing Syk inhibitor together with radiation and ICB could provide a durable anti-tumor immune response in an MYCN amplified murine model of NB. Methods: Myeloid Syk KO mice and syngeneic MYCN-amplified cell lines were used to elucidate the effect of myeloid Syk on the NB tumor microenvironment (TME). In addition, the effect of Syk inhibitor, R788, on anti-tumor immunity alone or in combination with anti-PDL1 mAb and radiation was also determined in murine NB models. The underlying mechanism of action of this novel therapeutic combination was also investigated. Results: Herein, we report that Syk is a marker of NB-associated macrophages and plays a crucial role in promoting immunosuppression in the NB TME. We found that the blockade of Syk in NB-bearing mice markedly impairs tumor growth. This effect is facilitated by macrophages that become immunogenic in the absence of Syk, skewing the suppressive TME towards immunostimulation and activating anti-tumor immune responses. Moreover, combining FDA-approved Syk inhibitor, R788 (fostamatinib) along with anti-PDL1 mAb provides a synergistic effect leading to complete tumor regression and durable anti-tumor immunity in mice bearing small tumors (50 mm3) but not larger tumors (250 mm3). However, combining radiation to R788 and anti-PDL1 mAb prolongs the survival of mice bearing large NB9464 tumors. Conclusion: Collectively, our findings demonstrate the central role of macrophage Syk in NB progression and demonstrate that Syk blockade can "reeducate" TAMs towards immunostimulatory phenotype, leading to enhanced T cell responses. These findings further support the clinical evaluation of fostamatinib alone or with radiation and ICB, as a novel therapeutic intervention in neuroblastoma.
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Inhibidores de Puntos de Control Inmunológico , Neuroblastoma , Animales , Ratones , Inhibidores de Puntos de Control Inmunológico/farmacología , Proteína Proto-Oncogénica N-Myc/metabolismo , Macrófagos , Neuroblastoma/metabolismo , Microambiente TumoralRESUMEN
Access to gender-affirming surgery is increasing for many transgender and nonbinary people in the United States, and radiation oncologists must be equipped to care for patients who have undergone such surgery in the region of their planned radiation treatment field. There are no guidelines for radiation treatment planning after gender-affirming surgery, and most oncologists do not receive training in the unique needs of transgender people with cancer. We review common gender-affirming genitopelvic surgeries for transfeminine people, including vaginoplasty, labiaplasty, and orchiectomy, and summarize the existing literature on the treatment of cancers of the neovagina, anus, rectum, prostate, and bladder in these patients. We also describe our systematic treatment approach and rationale for pelvic radiation treatment planning.
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Neoplasias , Cirugía de Reasignación de Sexo , Personas Transgénero , Masculino , Femenino , Humanos , Oncólogos de Radiación , Vagina , Canal Anal , Neoplasias/cirugíaRESUMEN
Little is known about the prevalence of pediatric radiation oncologists treating patients off study according to Children's Oncology Group (COG) trials before data are available regarding toxicity and efficacy of novel radiotherapy regimens. We conducted a 12-question survey of 358 pediatric radiation oncologists to characterize practice patterns regarding ongoing and completed COG protocols off study. With 130 responses (40.3%), the prevalence of providing treatment per protocol, but off study, before data are available in abstract or peer-reviewed form varied from 9.1% (for ACNS1422) to 88.1% (for AHOD1331). Future studies are needed to understand the effects of these practice patterns on outcomes.