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.

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.

Outcomes and Pattern of Care for Spinal Myxopapillary Ependymoma in the Modern Era-A Population-Based Observational Study

(1) Background: Myxopapillary ependymoma (MPE) is a rare tumor of the spine, typically slow-growing and low-grade. Optimal management strategies remain unclear due to limited evidence given the low incidence of the disease. (2) Methods: We analyzed data from 1197 patients with spinal MPE from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2020). Patient demographics, treatment modalities, and survival outcomes were examined using statistical analyses. (3) Results: Most patients were White (89.9%) with a median age at diagnosis of 42 years. Surgical resection was performed in 95% of cases. The estimated 10-year overall survival was 91.4%. Younger age (hazard ratio (HR) = 1.09, p < 0.001) and receipt of surgery (HR = 0.43, p = 0.007) were associated with improved survival. Surprisingly, male sex was associated with worse survival (HR = 1.86, p = 0.008) and a younger age at diagnosis compared to females. (4) Conclusions: This study, the largest of its kind, underscores the importance of surgical resection in managing spinal MPE. The unexpected association between male sex and worse survival warrants further investigation into potential sex-specific pathophysiological factors influencing prognosis. Despite limitations, our findings contribute valuable insights for guiding clinical management strategies for spinal MPE.

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.

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.