Hypofractionated radiotherapy for glioblastoma: A large institutional retrospective assessment of 2 approaches

Background

Glioblastoma (GBM) poses therapeutic challenges due to its aggressive nature, particularly for patients with poor functional status and/or advanced disease. Hypofractionated radiotherapy (RT) regimens have demonstrated comparable disease outcomes for this population while allowing treatment to be completed more quickly. Here, we report our institutional outcomes of patients treated with 2 hypofractionated RT regimens: 40 Gy/15fx (3w-RT) and 50 Gy/20fx (4w-RT).

Methods

A single-institution retrospective analysis was conducted of 127 GBM patients who underwent 3w-RT or 4w-RT. Patient characteristics, treatment regimens, and outcomes were analyzed. Univariate and multivariable Cox regression models were used to estimate progression-free survival (PFS) and overall survival (OS). The impact of chemotherapy and RT schedule was explored through subgroup analyses.

Results

Median OS for the entire cohort was 7.7 months. There were no significant differences in PFS or OS between 3w-RT and 4w-RT groups overall. Receipt and timing of temozolomide (TMZ) emerged as the variable most strongly associated with survival, with patients receiving adjuvant-only or concurrent and adjuvant TMZ having significantly improved PFS and OS (P < .001). In a subgroup analysis of patients that did not receive TMZ, patients in the 4w-RT group demonstrated a trend toward improved OS as compared to the 3w-RT group (P = .12).

Conclusions

This study demonstrates comparable survival outcomes between 3w-RT and 4w-RT regimens in GBM patients. Receipt and timing of TMZ were strongly associated with survival outcomes. The potential benefit of dose-escalated hypofractionation for patients not receiving chemotherapy warrants further investigation and emphasizes the importance of personalized treatment approaches.

Developing and Validating a Korean Version of the Assessment of Children's Emotional Skills

In this study, a Korean Assessment of Children's Emotional Skills (ACES) was developed by modifying the original ACES which was initially introduced in the United States. Specifically, the original ACES was translated into Korean and revised to better fit the Korean cultural context. The content validity of the revised Korean ACES was established via expert reviews. To test its reliability, the revised Korean ACES was conducted on 286 six-year-old children. A confirmatory factor analysis indicated that our newly developed Korean ACES can be used as an appropriate tool to measure Korean children's emotional skills. The Korean ACES can stimulate further studies on these emotional skills and contribute to various international collaborative studies that seek to compare the emotional skills of children from diverse cultural backgrounds.

Response of treatment-naive brain metastases to stereotactic radiosurgery

With improvements in survival for patients with metastatic cancer, long-term local control of brain metastases has become an increasingly important clinical priority. While consensus guidelines recommend surgery followed by stereotactic radiosurgery (SRS) for lesions >3 cm, smaller lesions (≤3 cm) treated with SRS alone elicit variable responses. To determine factors influencing this variable response to SRS, we analyzed outcomes of brain metastases ≤3 cm diameter in patients with no prior systemic therapy treated with frame-based single-fraction SRS. Following SRS, 259 out of 1733 (15%) treated lesions demonstrated MRI findings concerning for local treatment failure (LTF), of which 202 /1733 (12%) demonstrated LTF and 54/1733 (3%) had an adverse radiation effect. Multivariate analysis demonstrated tumor size (>1.5 cm) and melanoma histology were associated with higher LTF rates. Our results demonstrate that brain metastases ≤3 cm are not uniformly responsive to SRS and suggest that prospective studies to evaluate the effect of SRS alone or in combination with surgery on brain metastases ≤3 cm matched by tumor size and histology are warranted. These studies will help establish multi-disciplinary treatment guidelines that improve local control while minimizing radiation necrosis during treatment of brain metastasis ≤3 cm.

Additive Value of MR Simulation Prior to Chemoradiation in Evaluating Treatment Response and Pseudoprogression in High-Grade Gliomas

BACKGROUND

A dedicated MRI Simulation(MRsim) for radiation treatment(RT) planning in high-grade glioma(HGG) patients can detect early radiological changes, including tumor progression after surgery and before standard of care chemoradiation. This study aimed to determine the impact of using post-op MRI vs. MRsim as the baseline for response assessment and reporting pseudo-progression on follow-up imaging at one month(FU1) after chemoradiation.

METHODS

Histologically confirmed HGG patients were planned for six weeks of RT in a prospective study for adaptive RT planning. All patients underwent post-op MRI, MRsim, and follow-up MRI scans every 2-3 months. Tumor response was assessed by three independent blinded reviewers using Response Assessment in Neuro-Oncology(RANO) criteria when baseline was either post-op MRI or MRsim. Interobserver agreement was calculated using light's kappa.

RESULTS

30 patients (median age 60.5 years; IQR 54.5-66.3) were included. Median interval between surgery and RT was 34 days (IQR 27-41). Response assessment at FU1 differed in 17 patients (57%) when the baseline was post-op MRI vs. MRsim, including true progression vs. partial response(PR) or stable disease(SD) in 11 (37%) and SD vs. PR in 6 (20%) patients. True progression was reported in 19 patients (63.3%) on FU1 when the baseline was post-op MRI vs 8 patients (26.7%) when the baseline was MRsim (p=.004). Pseudo-progression was observed at FU1 in 12 (40%) vs. 4 (13%) patients, when the baseline was post-op MRI vs. MRsim (p=.019). Interobserver agreement between observers was moderate (κ = 0.579; p<0.001).

CONCLUSIONS

Our study demonstrates the value of acquiring an updated MR closer to RT in patients with HGG to improve response assessment, and accuracy in evaluation of pseudo-progression even at the early time point of first follow-up after RT. Earlier identification of patients with true progression would enable more timely salvage treatments including potential clinical trial enrolment to improve patient outcomes.

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors

BACKGROUND

Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy.

PURPOSE

Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data.

METHODS

We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts.

RESULTS

For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models.

CONCLUSIONS

Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Timing of radiotherapy (RT) after radical prostatectomy (RP): long-term outcomes in the RADICALS-RT trial (NCT00541047)

BACKGROUND

The optimal timing of radiotherapy (RT) after radical prostatectomy for prostate cancer has been uncertain. RADICALS-RT compared efficacy and safety of adjuvant RT versus an observation policy with salvage RT for prostate-specific antigen (PSA) failure.

PATIENTS AND METHODS

RADICALS-RT was a randomised controlled trial enrolling patients with ≥1 risk factor (pT3/4, Gleason 7-10, positive margins, preoperative PSA≥10 ng/ml) for recurrence after radical prostatectomy. Patients were randomised 1:1 to adjuvant RT ('Adjuvant-RT') or an observation policy with salvage RT for PSA failure ('Salvage-RT') defined as PSA≥0.1 ng/ml or three consecutive rises. Stratification factors were Gleason score, margin status, planned RT schedule (52.5 Gy/20 fractions or 66 Gy/33 fractions) and treatment centre. The primary outcome measure was freedom-from-distant-metastasis (FFDM), designed with 80% power to detect an improvement from 90% with Salvage-RT (control) to 95% at 10 years with Adjuvant-RT. Secondary outcome measures were biochemical progression-free survival, freedom from non-protocol hormone therapy, safety and patient-reported outcomes. Standard survival analysis methods were used; hazard ratio (HR)<1 favours Adjuvant-RT.

RESULTS

Between October 2007 and December 2016, 1396 participants from UK, Denmark, Canada and Ireland were randomised: 699 Salvage-RT, 697 Adjuvant-RT. Allocated groups were balanced with a median age of 65 years. Ninety-three percent (649/697) Adjuvant-RT reported RT within 6 months after randomisation; 39% (270/699) Salvage-RT reported RT during follow-up. Median follow-up was 7.8 years. With 80 distant metastasis events, 10-year FFDM was 93% for Adjuvant-RT and 90% for Salvage-RT: HR=0.68 [95% confidence interval (CI) 0.43-1.07, P=0.095]. Of 109 deaths, 17 were due to prostate cancer. Overall survival was not improved (HR=0.980, 95% CI 0.667-1.440, P=0.917). Adjuvant-RT reported worse urinary and faecal incontinence 1 year after randomisation (P=0.001); faecal incontinence remained significant after 10 years (P=0.017).

CONCLUSION

Long-term results from RADICALS-RT confirm adjuvant RT after radical prostatectomy increases the risk of urinary and bowel morbidity, but does not meaningfully improve disease control. An observation policy with salvage RT for PSA failure should be the current standard after radical prostatectomy.

TRIAL IDENTIFICATION

RADICALS, RADICALS-RT, ISRCTN40814031, NCT00541047.

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning

PURPOSE

The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

METHODS

Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature.

RESULTS

Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines.

CONCLUSIONS

Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Clinical Benefit from Docetaxel +/- Ramucirumab Is Not Associated with Mutation Status in Metastatic Non-Small-Cell Lung Cancer Patients Who Progressed on Platinum Doublets and Immunotherapy

Docetaxel +/- ramucirumab remains the standard-of-care therapy for patients with metastatic non-small-cell lung cancer (NSCLC) after progression on platinum doublets and immune checkpoint inhibitors (ICIs). The aim of our study was to investigate whether the cancer gene mutation status was associated with clinical benefits from docetaxel +/- ramucirumab. We also investigated whether platinum/taxane-based regimens offered a better clinical benefit in this patient population. A total of 454 patients were analyzed (docetaxel +/- ramucirumab n=381; platinum/taxane-based regimens n=73). Progression-free survival (PFS) and overall survival (OS) were compared among different subpopulations with different cancer gene mutations and between patients who received docetaxel +/- ramucirumab versus platinum/taxane-based regimens. Among patients who received docetaxel +/- ramucirumab, the top mutated cancer genes included TP53 (n=167), KRAS (n=127), EGFR (n=65), STK11 (n=32), ERBB2 (HER2) (n=26), etc. None of these cancer gene mutations or PD-L1 expression was associated with PFS or OS. Platinum/taxane-based regimens were associated with a significantly longer mQS (13.00 m, 95% Cl: 11.20-14.80 m versus 8.40 m, 95% Cl: 7.12-9.68 m, LogRank P=0.019) than docetaxel +/- ramcirumab. Key prognostic factors including age, histology, and performance status were not different between these two groups. In conclusion, in patients with metastatic NSCLC who have progressed on platinum doublets and ICIs, the clinical benefit from docetaxel +/- ramucirumab is not associated with the cancer gene mutation status. Platinum/taxane-based regimens may offer a superior clinical benefit over docetaxel +/- ramucirumab in this patient population.

Designing clinical trials for patients who are not average

The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.

Low household income increases the risk of tuberculosis recurrence: a retrospective nationwide cohort study in South Korea

OBJECTIVES

We assessed the impact of household income on tuberculosis (TB) recurrence and the long-term impact of TB on household income.

STUDY DESIGN

This was a retrospective nationwide cohort study of patients with drug-susceptible TB (DS-TB) and TB recurrence.

METHODS

Using the South Korean national TB cohort database, we identified a sub-set cohort of patients with newly diagnosed drug-susceptible TB between 2013 and 2016 and tracked their TB recurrence and longitudinal income data from 2007 to 2018. Income levels were evaluated as 'Medical aid' and quintile categories. To assess risk factors associated with TB recurrence, we used a sub-distribution hazard model, adjusting for the competing risks of death.

RESULTS

Of 66,690 patients successfully treated with DS-TB, 2095 (3.1 %) experienced recurrence during a median follow-up of 39 months. The incidence of TB recurrence was 982.1/100,000 person-years, with 50.3 % of the recurrences occurring within 1 year of treatment completion. The risk of TB recurrence increased with decreasing income levels, with the highest risk observed in the lowest income group. The effect of income on TB recurrence was prominent in males but not in females. Overall, patients with TB recurrence experienced a linear decline in income levels, compared with those without recurrence.

CONCLUSIONS

Household income during the initial TB episode was an important risk factor for TB recurrence, particularly in males.

The Future of MR-Guided Radiation Therapy

Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT.

Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation

Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts' evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts' segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality. Keywords: Brain Tumor Segmentation, Deep Learning Algorithms, Glioblastoma, Cancer, Machine Learning Clinical trial registration nos. NCT00756106 and NCT00662506 Supplemental material is available for this article. © RSNA, 2023.

Treatment outcome, recurrence and safety of multidrug-resistant TB treated with low-dose linezolid

BACKGROUND: Linezolid (LZD) is a key treatment option for patients with multidrug-resistant/rifampicin-resistant TB (MDR/RR-TB). We investigated the long-term treatment outcomes and safety of MDR/RR-TB treatment using low-dose LZD.METHODS: Medical records of patients with MDR/RR-TB treated with LZD ≥4 weeks between 2004 and 2018 at the Asan Medical Center, Seoul, Republic of Korea, were reviewed. Standard-dose and low-dose LZD groups were defined as patients initially administered LZD ≥600 mg/day or 300 mg/day, respectively.RESULTS: Among 94 patients, 65 were included in the low-dose LZD group; mean age was 43.1 ± 15.6 years, 53 (56.4%) were men and 77 (83.7%) were resistant to fluoroquinolone. The low-dose LZD group showed features of less severe disease, such as limited MDR-TB history and less severe radiological findings. There was no difference in treatment outcomes, relapse and safety between groups. In the low-dose LZD group, 54 (83.1%) succeeded treatment, of whom 48 (88.9%) were followed-up for a median of 38 months; there was no recurrence. Adverse drug reactions were reported in 41 (63.1%); peripheral neuropathy was most frequently reported (n = 31, 47.7%), while myelosuppression was reported in 12 (18.5%).CONCLUSION: Low-dose LZD in selected patients with less severe disease is both effective in the long-term and safe for the treatment of MDR/RR-TB.

Postoperative Management of Recurrence After Radiosurgery and Surgical Resection for Brain Metastases and Predicting Benefit From Adjuvant Radiation

Stereotactic radiosurgery (SRS) is often used as upfront treatment for brain metastases. Progression or radionecrosis after SRS is common and can prompt resection. However, postoperative management strategies after resection for SRS failure vary widely, and no standard practice has been established. In this approved study, we retrospectively reviewed patients who received SRS for a brain metastasis followed by resection of the same lesion. We extracted patient-, disease-, and treatment-related variables and information on disease-related outcomes. Univariate and multivariate analyses of clinicopathologic variables were used to create a model to predict factors associated with local failure (LF). A total of 225 patients with brain metastases treated with SRS from 2009 to 2017 followed by surgical resection were identified. Overall, 65% of cases had gross total resection (GTR) on postoperative imaging review. Twenty-one patients (9.3%) received adjuvant radiation therapy to the surgical cavity, and 204 (90.7%) were observed. Of these 204 patients, 118 had GTR with evidence of tumor within the pathology specimen. With a median follow-up of 13 months after resection, 47 patients (40%) developed LF after surgery. After salvage resection of a brain metastasis initially treated with SRS, the observed LF rate was 40% among those who had a GTR and evidence of tumor on pathologic examination. This LF rate is sufficiently high that adjuvant radiation to the surgical bed after salvage resection should be considered in these cases when there is tumor in the pathology, even after a GTR.

Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept

Background

Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers.

Materials and methods

We studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters ("habitats"). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value.

Results

Habitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E - 9) higher than the clinical and blood model (post-test probability of 22%).

Conclusion

Habitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.

Interstitial lung abnormalities after hospitalization for COVID-19 in patients with cancer: A prospective cohort study

INTRODUCTION

Survivors of SARS-CoV-2 pneumonia often develop persistent respiratory symptom and interstitial lung abnormalities (ILAs) after infection. Risk factors for ILA development and duration of ILA persistence after SARS-CoV-2 infection are not well described in immunocompromised hosts, such as cancer patients.

METHODS

We conducted a prospective cohort study of 95 patients at a major cancer center and 45 patients at a tertiary referral center. We collected clinical and radiographic data during the index hospitalization for COVID-19 pneumonia and measured pneumonia severity using a semi-quantitative radiographic score, the Radiologic Severity Index (RSI). Patients were evaluated in post-COVID-19 clinics at 3 and 6 months after discharge and underwent comprehensive pulmonary evaluations (symptom assessment, chest computed tomography, pulmonary function tests, 6-min walk test). The association of clinical and radiological factors with ILAs at 3 and 6 months post-discharge was measured using univariable and multivariable logistic regression.

RESULTS

Sixty-six (70%) patients of cancer cohort had ILAs at 3 months, of whom 39 had persistent respiratory symptoms. Twenty-four (26%) patients had persistent ILA at 6 months after hospital discharge. In adjusted models, higher peak RSI at admission was associated with ILAs at 3 (OR 1.5 per 5-point increase, 95% CI 1.1-1.9) and 6 months (OR 1.3 per 5-point increase, 95% CI 1.1-1.6) post-discharge. Fibrotic ILAs (reticulation, traction bronchiectasis, and architectural distortion) were more common at 6 months post-discharge.

CONCLUSIONS

Post-COVID-19 ILAs are common in cancer patients 3 months after hospital discharge, and peak RSI and older age are strong predictors of persistent ILAs.

Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology

In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.

Concordance of diagnostic modalities in atypical skin and soft tissue infections in hospitalized patients

Skin and soft tissue infections (SSTIs) have high rates of morbidity and mortality worldwide but lack reliable standards for diagnostic workup. As a result, atypical infections, more prevalent among immunocompromised patients, can be missed due to deviance from classic features only to be revealed later through inconsistently performed ancillary studies. Our objectives included to evaluate the sensitivities of clinical impression, histopathology, tissue culture, and molecular and non-molecular ancillary tests in diagnosing inpatient SSTIs, as well as to qualitatively discuss the unusual features making a subset of infections "atypical." To do so, we retrospectively reviewed the histopathologic reports and charts of inpatient dermatologic consults at a single tertiary care institution over a 3-year period. We identified a total of 111 cases of SSTIs evaluated by the inpatient dermatology consultation service with concurrent skin or soft tissue biopsy, with 32.4% representing atypical infections. Among these, clinical impression suggested infection in 9(25.0%), routine histopathology in 21(58.3%), specialized stains for microorganisms in 22(68.8%), and tissue culture in 15(68.2%). Due to incomplete picture that each modality by itself creates, we conclude that clinicians and pathologists should carry a low threshold for including SSTIs in their differential diagnoses and should evaluate with skin biopsy, special stains for microorganisms, and ancillary studies, particularly in critically ill individuals who necessitate timely diagnoses.

Multifocal and multicentric glioblastoma: Imaging signature, molecular characterization, patterns of spread, and treatment

Multifocal and multicentric glioblastoma (GBM) or collectively, m-GBM, is an imaging diagnosis present in up to 34% of patients with GBM. Compared to unifocal disease, patients with m-GBM have worse outcomes owing to the enhanced aggressive nature of the disease and its resistance to currently available treatments. To improve the understanding of its complex behavior, many associations have been established between the radiologic findings of m-GBM and its gross histology, genetic composition, and patterns of spread. Additionally, the holistic knowledge of the exact mechanisms of m-GBM genesis and progression is crucial for identifying potential targets permitting enhanced diagnosis and treatment. In this review, we aim to provide a comprehensive summary of the cumulative knowledge of the unique molecular biology and behavior of m-GBM and the association of these features with neuroimaging.

National Cancer Institute Collaborative Workshop on Shaping the Landscape of Brain Metastases Research: challenges and recommended priorities

Brain metastases are an increasing global public health concern, even as survival rates improve for patients with metastatic disease. Both metastases and the sequelae of their treatment are key determinants of the inter-related priorities of patient survival, function, and quality of life, mandating a multidimensional approach to clinical care and research. At a virtual National Cancer Institute Workshop in September, 2022, key stakeholders convened to define research priorities to address the crucial areas of unmet need for patients with brain metastases to achieve meaningful advances in patient outcomes. This Policy Review outlines existing knowledge gaps, collaborative opportunities, and specific recommendations regarding consensus priorities and future directions in brain metastases research. Achieving major advances in research will require enhanced coordination between the ongoing efforts of individual organisations and consortia. Importantly, the continual and active engagement of patients and patient advocates will be necessary to ensure that the directionality of all efforts reflects what is most meaningful in the context of patient care.

Nivolumab and ipilimumab with concurrent stereotactic radiosurgery for intracranial metastases from non-small cell lung cancer: analysis of the safety cohort for non-randomized, open-label, phase I/II trial

BACKGROUND

Up to 20% of patients with non-small cell lung cancer (NSCLC) develop brain metastasis (BM), for which the current standard of care is radiation therapy with or without surgery. There are no prospective data on the safety of stereotactic radiosurgery (SRS) concurrent with immune checkpoint inhibitor therapy for BM. This is the safety cohort of the phase I/II investigator-initiated trial of SRS with nivolumab and ipilimumab for patients with BM from NSCLC.

PATIENTS AND METHODS

This single-institution study included patients with NSCLC with active BM amenable to SRS. Brain SRS and systemic therapy with nivolumab and ipilimumab were delivered concurrently (within 7 days). The endpoints were safety and 4-month intracranial progression-free survival (PFS).

RESULTS

Thirteen patients were enrolled in the safety cohort, 10 of whom were evaluable for dose-limiting toxicities (DLTs). Median follow-up was 23 months (range 9.7-24.3 months). The median interval between systemic therapy and radiation therapy was 3 days. Only one patient had a DLT; hence, predefined stopping criteria were not met. In addition to the patient with DLT, three patients had treatment-related grade ≥3 adverse events, including elevated liver function tests, fatigue, nausea, adrenal insufficiency, and myocarditis. One patient had a confirmed influenza infection 7 months after initiation of protocol treatment (outside the DLT assessment window), leading to pneumonia and subsequent death from hemophagocytic lymphohistiocytosis. The estimated 4-month intracranial PFS rate was 70.7%.

CONCLUSION

Concurrent brain SRS with nivolumab/ipilimumab was safe for patients with active NSCLC BM. Preliminary analyses of treatment efficacy were encouraging for intracranial treatment response.

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study

BACKGROUND

Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context.

METHODS

In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics.

FINDINGS

Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features.

INTERPRETATION

This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer.

FUNDING

National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO)

PURPOSE

Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements.

MATERIALS AND METHODS

Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach in which the segmentation results may be manually refined by radiologists. After the implementation of the framework in Docker containers, it was applied to two retrospective glioma data sets collected from the Washington University School of Medicine (WUSM; n = 384) and The University of Texas MD Anderson Cancer Center (MDA; n = 30), comprising preoperative MRI scans from patients with pathologically confirmed gliomas.

RESULTS

The scan-type classifier yielded an accuracy of >99%, correctly identifying sequences from 380 of 384 and 30 of 30 sessions from the WUSM and MDA data sets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. The mean Dice scores were 0.882 (±0.244) and 0.977 (±0.04) for whole-tumor segmentation for WUSM and MDA, respectively.

CONCLUSION

This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology data sets and demonstrating high potential for integration as an assistive tool in clinical practice.

PocketNet: A Smaller Neural Network for Medical Image Analysis

Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings.

Customizable landmark-based field aperture design for automated whole-brain radiotherapy treatment planning

PURPOSE

To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design.

METHODS

The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals.

RESULTS

The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans.

CONCLUSION

This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Association between COVID-19 and Postoperative Neurological Complications and Antipsychotic Medication Use after Cancer Surgery: A Retrospective Study

INTRODUCTION

Millions of Americans infected with the severe acute respiratory syndrome-associated coronavirus-19 (COVID-19) need oncologic surgery. Patients with acute or resolved COVID-19 illness complain of neuropsychiatric symptoms. How surgery affects postoperative neuropsychiatric outcomes such as delirium is unknown. We hypothesize that patients with a history of COVID-19 could have an exaggerated risk of developing postoperative delirium after undergoing major elective oncologic surgery.

METHODS

We conducted a retrospective study to determine the association between COVID-19 status and antipsychotic drugs during postsurgical hospitalization as a surrogate of delirium. Secondary outcomes included 30 days of postoperative complications, length of stay, and mortality. Patients were grouped into pre-pandemic non-COVID-19 and COVID-19-positive groups. A 1:2 propensity score matching was used to minimize bias. A multivariable logistic regression model estimated the effects of important covariates on the use of postoperative psychotic medication.

RESULTS

A total of 6003 patients were included in the study. Pre- and post-propensity score matching demonstrated that a history of preoperative COVID-19 did not increase the risk of antipsychotic medications postoperatively. However, respiratory and overall 30-day complications were higher in COVID-19 individuals than in pre-pandemic non-COVID-19 patients. The multivariate analysis showed that the odds of using postoperative antipsychotic medication use for the patients who had COVID-19 compared to those who did not have the infection were not significantly different.

CONCLUSION

A preoperative diagnosis of COVID-19 did not increase the risk of postoperative antipsychotic medication use or neurological complications. More studies are needed to reproduce our results due to the increased concern of neurological events post-COVID-19 infection.

Assessment of intra-fraction motion during frameless image guided Gamma Knife stereotactic radiosurgery

As frameless stereotactic radiosurgery increase in use, the aim of this study was to evaluate intra-fraction motion through cone-beam CT (CBCT) and high-definition motion management (HDMM) systems. Intra-fraction motion measured between localization, repeat localization and post-treatment CBCTs were correlated to intra-faction motion indicated by the HDMM files using the Pearson coefficient (r). A total of 302 plans were reviewed from 263 patients (114 male, 149 female); 216 pairs of localization-repeat localization, and 260 localization-post-treatment CBCTs were analyzed against HDMM logs. We found the magnitude of intra-fraction motion detected by the HDMM system were larger than the corresponding CBCT results.

On the importance of interpretable machine learning predictions to inform clinical decision making in oncology

Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.

Patient Outcomes in Laparoscopic Appendectomy With Acute Surgical Care Model Compared to Traditional Call

INTRODUCTION

Shift-based models for acute surgical care (ACS), where surgical emergencies are treated by a dedicated team of surgeons working shifts, without a concurrent elective practice, are becoming more common nationwide. We compared the outcomes for appendectomy, one of the most common emergency surgical procedures, between the traditional (TRAD) call and ACS model at the same institution during the same time frame.

METHODS

A retrospective review of patients who underwent laparoscopic appendectomy for acute appendicitis during 2017-2018. ACS and TRAD-patient demographics, clinical presentation, operative details, and outcomes were compared using independent sample t-tests, Wilcoxon rank-sum tests and Fisher's exact or χ² tests. Multiple exploratory regression models were constructed to examine the effects of confounding variables.

RESULTS

Demographics, clinical presentation, and complication rates were similar between groups except for a longer duration of symptoms prior to arrival in the TRAD group (Δ = 0.5 d, P = 0.006). Time from admission to operating room (Δ = -1.85 h, P = 0.003), length of hospital stay (Δ = -2.0 d, P < 0.001), and total cost (Δ = $ -2477.02, P < 0.001) were significantly lower in the ACS group compared to the TRAD group. Furthermore, perforation rates were lower in ACS (8.3% versus 28.6%, P = 0.003). Differences for the outcomes remained significant even after controlling for duration of symptoms prior to arrival (P < 0.05).

CONCLUSIONS

Acute appendicitis managed using the ACS shift-based model seems to be associated with reduced time to operation, hospital stay, and overall cost, with equivalent success rates, compared to TRAD.

A Prospective Study of Conventionally Fractionated Dose Constraints for Reirradiation of Primary Brain Tumors in Adults

PURPOSE

Dose constraints for reirradiation of recurrent primary brain tumors are not well-established. This study was conducted to prospectively evaluate composite dose constraints for conventionally fractionated brain reirradiation.

METHODS AND MATERIALS

A single-institution, prospective study of adults with previously irradiated, recurrent brain tumors was performed. For 95% of patients, electronic dosimetry records from the first course of radiation (RT1) were obtained and deformed onto the simulation computed tomography for the second course of radiation (RT2). Conventionally fractionated treatment plans for RT2 were developed that met protocol-assigned dose constraints for RT2 alone and the composite dose of RT1 + RT2. Prospective composite dose constraints were based on histology, interval since RT1, and concurrent bevacizumab. Patients were followed with magnetic resonance imaging including spectroscopy and perfusion studies. Primary endpoint was the rate of symptomatic brain necrosis at 6 months after RT2.

RESULTS

Patients were enrolled from March 2017 to May 2018; 20 were evaluable. Eighteen had glioma, 1 had atypical choroid plexus papilloma, and 1 had hemangiopericytoma. Nineteen patients were treated with volumetric modulated arc therapy, and one was treated with protons. Median RT1 dose was 57 Gy (range, 50-60 Gy). Median RT1-RT2 interval was 49 months (range, 9-141 months). Median RT2 dose was 42.4 Gy (range, 36-60 Gy). Median planning target volume was 186 cc (range, 8-468 cc). Nineteen of 20 patients (95%) were free of grade 3+ central nervous system necrosis. One patient had grade 3+ necrosis 2 months after RT2; the patient recovered fully and lived another 18 months until dying of disease progression. Median overall survival from RT2 start for all patients was 13.3 months (95% credible interval, 6.3-20.7); for patients with glioblastoma, 11.5 months (95% credible interval, 6.1-20.1).

CONCLUSIONS

Brain reirradiation can be safely performed with conventionally fractionated regimens tailored to previous dose distributions. The prospective composite dose constraints described here are a starting point for future studies of conventionally fractionated reirradiation.

Changes In Outcomes And Factors Associated With Survival In Melanoma Patients With Brain Metastases

BACKGROUD

Treatment options for patients with melanoma brain metastasis (MBM) have changed significantly in the last decade. Few studies have evaluated changes in outcomes and factors associated with survival in MBM patients over time. The aim of this study is to evaluate changes in clinical features and overall survival (OS) for MBM patients.

METHODS

Patients diagnosed with MBMs from 1/1/2009-12/31/2013 (Prior Era; PE) and 1/1/2014-12/31/2018 (Current Era; CE) at The University of Texas MD Anderson Cancer Center were included in this retrospective analysis. The primary outcome measure was OS. Log-rank test assessed differences between groups; multivariable analyses were performed with Cox proportional hazards models and recursive partitioning analysis (RPA).

RESULTS

791 MBM patients (PE, n=332; CE, n=459) were included in analysis. Median OS from MBM diagnosis was 10.3 months (95% CI, 8.9 - 12.4) and improved in the CE versus PE (14.4 vs. 10.3 months, P < .001). Elevated serum LDH was the only factor associated with worse OS in both PE and CE patients. Factors associated with survival in CE MBM patients included patient age, primary tumor Breslow thickness, prior immunotherapy, leptomeningeal disease (LMD), symptomatic MBMs, and whole brain radiation therapy (WBRT). Several factors associated with OS in the PE were not significant in the CE. RPA demonstrated that elevated serum LDH and prior immunotherapy treatment are the most important determinants of survival in CE MBM patients.

CONCLUSIONS

OS and factors associated with OS have changed for MBM patients. This information can inform contemporary patient management and clinical investigations.

Perioperative considerations in patients with chemotherapy-induced cognitive impairment: a narrative review

Patients with cancer may suffer from a decline in their cognitive function after various cancer therapies, including surgery, radiation, and chemotherapy, and in some cases, this decline in cognitive function persists even years after completion of treatment. Chemobrain or chemotherapy-induced cognitive impairment, a well-established clinical syndrome, has become an increasing concern as the number of successfully treated cancer patients has increased significantly. Chemotherapy-induced cognitive impairment can originate from direct neurotoxicity, neuroinflammation, and oxidative stress, resulting in alterations in grey matter volume, white matter integrity, and brain connectivity. Surgery has been associated with exacerbating the inflammatory response associated with chemotherapy and predisposes patients to develop postoperative cognitive dysfunction. As the proportion of patients living longer after these therapies increases, the magnitude of impact and growing concern of post-treatment cognitive dysfunction in these patients has also come to the fore. We review the clinical presentation, potential mechanisms, predisposing factors, diagnostic methods, neuropsychological testing, and imaging findings of chemotherapy-induced cognitive impairment and its intersection with postoperative cognitive dysfunction.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)¹ and Butner et al. (2021).².

NIMG-79. SPATIALLY MAPPED PREDICTIONS OF EVOLVING TUMOR RESPONSE OF HIGH-GRADE GLIOMA VIA IMAGE-DRIVEN MATHEMATICAL MODELING

Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) imaging timepoints for achieving diagnostic certainty, which delays therapeutic interventions. Mathematical modeling (MM) of tumor growth and treatment response can provide spatiotemporal information of HGG evolution in response to treatment, thus allowing for prospective early identification of resilient tumor subregions. AIMS: We aim to initialize and calibrate an image-driven MM framework to forecast HGG response, both at the end of chemoradiotherapy (CRT) and at 3-month FU.

METHODS

In a prospective clinical study, weekly mpMRIs (post-contrast T1, T2 FLAIR, and diffusion) for patients with HGG receiving CRT were used to describe tumor extent and cellularity. This data collected from baseline (pre-CRT) till week 3 (mid-CRT) was used to calibrate a model family to forecast HGG response for each individual patient at week 6 (end CRT) and at 3-month FU.

RESULTS

Error between the forecasted and observed responses was assessed globally using percent error in tumor volume, and at the local level by Pearson correlation coefficient (PCC). In an initial cohort of 11 patients, our MM framework predictions had a percent error in tumor volume of less than 8.6% and at week 6 RT and less than 20% at 3 months FU. The PCCs were 0.84 at week 6 RT and 0.72 at 3 months FU.

CONCLUSIONS

Temporal consistency across this early evaluation of the model predictions show promise of image-driven MM for HGG response forecasting to guide timely personalized assessment and adjustment of treatment.

NEIM-05 FEASIBILITY OF NAVIGATED TRANSCRANIAL MAGNETIC STIMULATION (NTMS) BASED DIFFUSION TENSOR IMAGING (DTI) TRACTOGRAPHY OF MOTOR PATHWAYS IN PATIENTS UNDERGOING STEREOTACTIC RADIOSURGERY: A CROSS-SECTIONAL DOSIMETRIC AND PATIENT OUTCOMES ANALYSIS

BACKGROUND

No current dose limitations exist for the motor tracts during stereotactic radiosurgery (SRS) planning due to challenges in localizing this region of interest by conventional imaging. Navigated Transcranial Magnetic Stimulation (nTMS) is a non-invasive tool that utilizes electromyographic signal combined with magnetic resonance diffusion tensor imaging (DTI) to functionally map cortical motor tracts. Although nTMS is utilized for functional mapping prior to brain tumor resection, it has not been implemented in SRS planning.

OBJECTIVES

To determine the feasibility of performing nTMS-based DTI in patients treated with SRS and examine the relationship between dose to functionally-defined motor tracts and patient outcomes measured by objective hand function testing and patient reported outcomes (PROs).

METHODS

16 patients treated with SRS to a brain metastasis located near anatomically-defined motor tracts were enrolled on an IRB-approved clinical trial. At median follow-up of 5.4m after SRS, patients underwent nTMS testing, brain MRI with DTI, functional outcomes testing (Pinch Dynamometer,9-Hole Peg Test), and quality-of-life (QOL) PROs (EQ-5D-5L, MDASI-BT). nTMS-seeded DTI tractography was generated (Brainlab iPlan) and imported into GammaPlan for dosimetric evaluation.

RESULTS

Tractography reconstitution was attempted for 8/16 patients and successful in 7/8 (87.5%). One patient who had prior resection of a lesion in the right pre-central gyrus failed to map in the right cortex and was unable to complete functional testing for the affected extremity. Median D_max to the treated motor tracts was 4.6Gy [0.5-13.4Gy]. Median D_mean was 0.9Gy [0-1.2Gy]. Increased D_max correlated with deficits in lateral pinch strength (R²=0.76) and 9-Hole Peg testing time (R²=0.61). Increased D_mean correlated with increased MDASI-BT interference scores (R²=0.93) and EQ5D5L score (R²=0.94) indicating worsened QOL.

CONCLUSIONS

nTMS testing was feasible and dose to nTMS-defined motor tracts correlated with subjective and objective patient outcomes. Future steps will include characterization of motor tract dose tolerance for SRS.

NEIM-06 COMBINING CLINICAL VARIABLES AND RADIOMIC FEATURES TO HELP DISTINGUISH RADIATION NECROSIS FROM TUMOR IN PATIENTS WITH MELANOMA BRAIN METASTASES TREATED WITH RADIOSURGERY

BACKGROUND

Following Gamma Knife SRS (GK-SRS), the conventional imaging characteristics of radiation necrosis (RN) mimic those of tumor progression, introducing considerable uncertainty in diagnosis. Previous studies have identified clinical variables associated with RN; however, diagnosis primarily relied on interpretation of imaging with only a minority confirmed using the gold standard of pathological examination. Furthermore, the cohorts of these studies included a mix of primary histologies.

PURPOSE

To identify the combination of clinical variables and radiomic features most predictive of RN in patients with melanoma brain metastasis (BM) with GK-SRS in order to train a machine learning classifier to distinguish RN from tumor progression.

METHODS

We retrospectively studied 86 patients with a melanoma BM that received initial GK-SRS followed by resection, thereby pathologically confirming tumor or RN. Clinical variables including lesion volume, age at surgery, GK-SRS dose, lesion hemorrhage, lesion location, gender, BM velocity, and drug therapy type were obtained from chart review. We extracted radiomic features from contrast-enhanced T1-weighted MR images using PyRadiomics. A consensus clustering algorithm identified representative radiomic features. Non-parametric hypothesis testing was performed on the clinical variables and representative radiomic features.

RESULTS

Of the 86 patients, 17 (19.8%) patients exhibited RN and 69 exhibited tumor progression. Lesion volume was associated with development of RN (p = 0.038&lt;0.05) with a median volume of 1.5 cc (0.01-26.71 cc). Clustering analysis identified seven representative radiomic features; five were found to have statistically significant association with development of RN.

CONCLUSION

In this dataset with pathologically confirmed diagnoses in a histologically homogeneous patient cohort, we reproduced previously reported findings that the clinical variable of lesion volume is associated with RN and we identified several radiomic features associated with RN in patients with melanoma BM. We are using these variables and features to train a machine learning classifier to distinguish RN from tumor.

Hypothetical generalized framework for a new imaging endpoint of therapeutic activity in early phase clinical trials in brain tumors

Imaging response assessment is a cornerstone of patient care and drug development in oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new treatments and guide decision making for patients and candidate therapies. This is important in brain cancer, where associations between tumor size/growth and emerging neurological deficits are strong. Accurately measuring the impact of a new therapy on tumor growth early in clinical development, where patient numbers are small, would be valuable for decision making regarding late-stage development activation. Current attempts to measure the impact of a new therapy have limited influence on clinical development, as determination of progression, stability or response does not currently account for individual tumor growth kinetics prior to the initiation of experimental therapies. Therefore, we posit that imaging-based response assessment, often used as a tool for estimating clinical effect, is incomplete as it does not adequately account for growth trajectories or biological characteristics of tumors prior to the introduction of an investigational agent. Here, we propose modifications to the existing framework for evaluating imaging assessment in primary brain tumors that will provide a more reliable understanding of treatment effects. Measuring tumor growth trajectories prior to a given intervention may allow us to more confidently conclude whether there is an anti-tumor effect. This updated approach to imaging-based tumor response assessment is intended to improve our ability to select candidate therapies for later-stage development, including those that may not meet currently sought thresholds for "response" and ultimately lead to identification of effective treatments.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Association between telehealth and adherence with patient-reported outcomes (PRO)-based remote symptom monitoring among adolescent/young adults (AYA), middle age, and older adults with cancer

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Background: PRO-based remote symptom monitoring favorably impacts quality of life, healthcare utilization, and overall survival in patients (pts) with cancer. However remote PRO completion rates outside of a clinical trial remained widely varied. With the wide adoption of telehealth in cancer care during the pandemic, telehealth’s impact on health behaviors such adherence w remote PROs is not fully characterized. To that end, we investigated PRO completion patterns in routine cancer care, pre- and during the pandemic. Methods: We queried a prospectively maintained institutional database of all PROs remotely delivered to pts at our institution from 1/1/18 to 12/31/21. Pts were divided into 2 time cohorts (“pre-pandemic” 1/1/18 to 3/31/20, “during pandemic” 4/1/20 to 12/31/21) and 3 age cohorts (AYA 15-39y, midage 40-64y, older adults 65y+). We calculated descriptive statistics and compared (t-test, ANOVA) between time and age cohorts and independent variables. Results: Overall 93,875 unique patients over 4 years received 1+ remote PROs as a part of their routine cancer care. PRO response rate increased from 35% prepandemic (12011 of 34742 pts responding) to 67% during pandemic (p <0.00001). To understand patient-level response patterns, we selected one representative global health PRO tool used widely across clinics in our institution and analyzed completion in a representative month over 4 years, 2 before (Oct ’18, ‘19) and 2 mid-pandemic (Oct ’20, ‘21). Overall 2738 pts (median age 60y, range 17-94y; 290 AYA 15-39y, 1444 midage 40-64y, 1004 older adults 65y+) were sent 3249 PROs during these 4m, 1378 PROs to 1075 pts in 2 pre-pandemic months & 1871 to 1663 pts in 2 mid-pandemic months. Overall, PRO response rate increased from 52% pre-pandemic to 81% during, non-responders dropping from 48% to 19%, and response rate without any reminder from the team increasing from 13% pre-pandemic to 79% during. Across all 3 age cohorts, overall PRO response rates increased (AYA up 21%, midage up 27%, seniors up 35%, p 0.012), PRO non-response rate decreased (AYA by 21%, midage by 27%, seniors by 35%, p 0.01), and PRO response rate without reminders from clinic team increased significantly (AYA, by 71%, midage by 78%, senior by 61%, p <0.00001). When further analyzing by visit type during pandemic, the improvements in overall PRO response rates are driven almost exclusively by telehealth where in-person PRO completion decreased by 19% (pre-pandemic 52%, during 33%) while pts who had an upcoming virtual visit had 94% PRO response rate (p < 0.00001). Conclusions: Substantially higher adherence with PRO-based remote symptom monitoring was seen during the pandemic with virtual visits accounting substantially for this broad adherence and the highest increases seen in older adults, highlighting the implications of telehealth on cancer care.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Paradoxical Association Between Relative Cerebral Blood Volume Dynamics Following Chemoradiation and Increased Progression-Free Survival in Newly Diagnosed IDH Wild-Type MGMT Promoter Methylated Glioblastoma With Measurable Disease

Background and Purpose

While relative cerebral blood volume (rCBV) may be diagnostic and prognostic for survival in glioblastoma (GBM), changes in rCBV during chemoradiation in the subset of newly diagnosed GBM with subtotal resection and the impact of MGMT promoter methylation status on survival have not been explored. This study aimed to investigate the association between rCBV response, MGMT methylation status, and progression-free (PFS) and overall survival (OS) in newly diagnosed GBM with measurable enhancing lesions.

Methods

1,153 newly diagnosed IDH wild-type GBM patients were screened and 53 patients (4.6%) had measurable post-surgical tumor (>1mL). rCBV was measured before and after patients underwent chemoradiation. Patients with a decrease in rCBV >10% were considered rCBV Responders, while patients with an increase or a decrease in rCBV <10% were considered rCBV Non-Responders. The association between change in enhancing tumor volume, change in rCBV, MGMT promotor methylation status, and PFS or OS were explored.

Results

A decrease in tumor volume following chemoradiation trended towards longer OS (p=0.12; median OS=26.8 vs. 16.3 months). Paradoxically, rCBV Non-Responders had a significantly improved PFS compared to Responders (p=0.047; median PFS=9.6 vs. 7.2 months). MGMT methylated rCBV Non-Responders exhibited a significantly longer PFS compared to MGMT unmethylated rCBV Non-Responders (p<0.001; median PFS=0.5 vs. 7.1 months), and MGMT methylated rCBV Non-Responders trended towards longer PFS compared to methylated rCBV Responders (p=0.089; median PFS=20.5 vs. 13.8 months).

Conclusions

This preliminary report demonstrates that in newly diagnosed IDH wild-type GBM with measurable enhancing disease after surgery (5% of patients), an enigmatic non-response in rCBV was associated with longer PFS, particularly in MGMT methylated patients.