Artificial intelligence in cancer imaging: Clinical challenges and applications.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Densely interconnected transcriptional circuits control cell states in human hematopoiesis.

Though many individual transcription factors are known to regulate hematopoietic differentiation, major aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly coexpressed genes, some of which are restricted to a single lineage but most of which are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states. These findings suggest a more complex regulatory system for hematopoiesis than previously assumed.

The ASCENT Trial: a phase 2 study of induction and consolidation afatinib and chemoradiation with or without surgery in stage III EGFR-mutant NSCLC.

BACKGROUND: The role of tyrosine kinase inhibitors (TKIs) in early-stage and metastatic oncogene-driven non-small cell lung cancer (NSCLC) is established, but it remains unknown how best to integrate TKIs with concurrent chemoradiotherapy (cCRT) in locally advanced disease. The phase 2 ASCENT trial assessed the efficacy and safety of afatinib and cCRT with or without surgery in locally advanced epidermal growth factor receptor (EGFR)-mutant NSCLC. PATIENTS AND METHODS: Adults ≥18 years with histologically confirmed stage III (AJCC 7th edition) NSCLC with activating EGFR mutations were enrolled at Mass General and Dana-Farber/Brigham Cancer Centers, Boston, Massachusetts. Patients received induction afatinib 40 mg daily for 2 months, then cisplatin 75 mg/m2 and pemetrexed 500 mg/m2 IV every 3 weeks during RT (definitive or neoadjuvant dosing). Patients with resectable disease underwent surgery. All patients were offered consolidation afatinib for 2 years. The primary endpoint was the objective response rate (ORR) to induction TKI. Secondary endpoints were safety, conversion to operability, progression-free survival (PFS), and overall survival (OS). Analyses were performed on the intention-to-treat population. RESULTS: Nineteen patients (median age 56 years; 74% female) were enrolled. ORR to induction afatinib was 63%. Seventeen patients received cCRT; 2/9 previously unresectable became resectable. Ten underwent surgery; 6 had a major or complete pathological response. Thirteen received consolidation afatinib. With a median follow-up of 5.0 years, median PFS and OS were 2.6 (95% CI, 1.4-3.1) and 5.8 years (2.9-NR), respectively. Sixteen recurred or died; 6 recurrences were isolated to CNS. The median time to progression after stopping consolidation TKI was 2.9 months (95% CI, 1.1-7.2). Four developed grade 2 pneumonitis. There were no treatment-related deaths. CONCLUSION: We explored the efficacy of combining TKI with cCRT in oncogene-driven NSCLC. Induction TKI did not compromise subsequent receipt of multimodality therapy. PFS was promising, but the prevalence of CNS-only recurrences and rapid progression after TKI discontinuation speak to unmet needs in measuring and eradicating micrometastatic disease.

Acute toxicity comparison of magnetic resonance-guided adaptive versus fiducial or computed tomography-guided non-adaptive prostate stereotactic body radiotherapy: A systematic review and meta-analysis.

BACKGROUND: Stereotactic body radiotherapy (SBRT) is gaining wider adoption for prostate cancer management but there remain significant toxicity risks when delivering prostate SBRT with standard techniques. Magnetic resonance-guided daily adaptive SBRT (MRg-A-SBRT) offers technological advantages in precision of radiation dose delivery, but the toxicity profile associated with MRg-A-SBRT compared to more standardly used fiducial or computed tomography-guided non-adaptive prostate SBRT (CT-SBRT) remains unknown. METHODS: A meta-analysis to compare acute toxicity rates associated with MRg-A-SBRT and CT-SBRT for prostate cancer was performed in compliance with PRISMA guidelines. MEDLINE (PubMed) and Google Scholar were searched for prospective studies of prostate SBRT that were published between January 1, 2018 and August 31, 2022. Random effects and fixed effects models were used to estimate pooled toxicity rates, and meta-regression was performed to compare toxicity between MRg-A-SBRT and CT-SBRT study groups. RESULTS: Twenty-nine prospective studies were identified that met the inclusion criteria and included a total of 2547 patients. The pooled estimates for acute grade 2 or higher (G2+) genitourinary (GU) and gastrointestinal (GI) toxicity for MRg-A-SBRT were 16% (95% confidence interval [CI], 10%-24%) and 4% (95% CI, 2%-7%) and for CT-SBRT they were 28% (95% CI, 23%-33%) and 9% (95% CI, 6%-12%), respectively. On meta-regression, the odds ratios for acute G2+ GU and GI toxicities comparing MRg-A-SBRT and CT-SBRT were 0.56 (95% CI, 0.33-0.97, p = .04) and 0.40 (95% CI, 0.17-0.96, p = .04), respectively. CONCLUSION: MRg-A-SBRT is associated with a significantly reduced risk of acute G2+ GU or GI toxicity compared to CT-SBRT. Longer follow-up will be needed to evaluate late toxicity and disease control outcomes. PLAIN LANGUAGE SUMMARY: Magnetic resonance imaging-guided daily adaptive prostate stereotactic radiation (MRg-A-SBRT) is a treatment that may allow for delivery of prostate radiation more precisely than other radiotherapy techniques, but it is unknown whether this reduces side effects compared to standardly used computed tomography-guided SBRT (CT-SBRT). In this systematic review and meta-analysis combining data from 29 clinical trials including 2547 patients, it was found that the risk of short-term urinary side effects was reduced by 44% and the risk of short-term bowel side effects was reduced by 60% with MRg-A-SBRT compared to CT-SBRT.

Comparison of MR-soft tissue based versus biliary stent based alignment for image guidance in pancreatic SBRT.

PURPOSE: The role of biliary stents in image-guided localization for pancreatic cancer has been inconclusive. To date, stent accuracy has been largely evaluated against implanted fiducials on cone beam computed tomography. We aim to use magnetic resonance (MR) soft tissue as a direct reference to examine the geometric and dosimetric impacts of stent-based localization on the newly available MR linear accelerator. METHODS: Thirty pancreatic cancer patients (132 fractions) treated on our MR linear accelerator were identified to have a biliary stent. In our standard adaptive workflow, patients were set up to the target using soft tissue for image registration and structures were re-contoured on daily MR images. The original plan was then projected on treatment anatomy and dose predicted, followed by plan re-optimization and treatment delivery. These online predicted plans were soft tissue-based and served as reference plans. Retrospective image registration to the stent was performed offline to simulate stent-based localization and the magnitude of shifts was taken as the geometric accuracy of stent localization. New predicted plans were generated based on stent-alignment for dosimetric comparison. RESULTS: Shifts were within 3 mm for 90% of the cases (mean = 1.5 mm); however, larger shifts up to 7.2 mm were observed. Average PTV coverage dropped by 1.1% with a maximum drop of 26.8%. The mean increase in V35Gy was 0.15, 0.05, 0.02, and 0.02 cc for duodenum, stomach, small bowel and large bowel, respectively. Stent alignment was significantly worse for all metrics except for small bowel (p = 0.07). CONCLUSIONS: Overall discrepancy between stent- and soft tissue-alignment was modest; however, large discrepancies were observed for select cases. While PTV coverage loss may be compensated for by using a larger margin, the increase in dose to gastrointestinal organs at risk may limit the role of biliary stents in image-guided localization.

Durvalumab plus tremelimumab alone or in combination with low-dose or hypofractionated radiotherapy in metastatic non-small-cell lung cancer refractory to previous PD(L)-1 therapy: an open-label, multicentre, randomised, phase 2 trial.

BACKGROUND: Patients with non-small-cell lung cancer (NSCLC) that is resistant to PD-1 and PD-L1 (PD[L]-1)-targeted therapy have poor outcomes. Studies suggest that radiotherapy could enhance antitumour immunity. Therefore, we investigated the potential benefit of PD-L1 (durvalumab) and CTLA-4 (tremelimumab) inhibition alone or combined with radiotherapy. METHODS: This open-label, multicentre, randomised, phase 2 trial was done by the National Cancer Institute Experimental Therapeutics Clinical Trials Network at 18 US sites. Patients aged 18 years or older with metastatic NSCLC, an Eastern Cooperative Oncology Group performance status of 0 or 1, and progression during previous PD(L)-1 therapy were eligible. They were randomly assigned (1:1:1) in a web-based system by the study statistician using a permuted block scheme (block sizes of three or six) without stratification to receive either durvalumab (1500 mg intravenously every 4 weeks for a maximum of 13 cycles) plus tremelimumab (75 mg intravenously every 4 weeks for a maximum of four cycles) alone or with low-dose (0·5 Gy delivered twice per day, repeated for 2 days during each of the first four cycles of therapy) or hypofractionated radiotherapy (24 Gy total delivered over three 8-Gy fractions during the first cycle only), 1 week after initial durvalumab-tremelimumab administration. Study treatment was continued until 1 year or until progression. The primary endpoint was overall response rate (best locally assessed confirmed response of a partial or complete response) and, along with safety, was analysed in patients who received at least one dose of study therapy. The trial is registered with ClinicalTrials.gov, NCT02888743, and is now complete. FINDINGS: Between Aug 24, 2017, and March 29, 2019, 90 patients were enrolled and randomly assigned, of whom 78 (26 per group) were treated. This trial was stopped due to futility assessed in an interim analysis. At a median follow-up of 12·4 months (IQR 7·8-15·1), there were no differences in overall response rates between the durvalumab-tremelimumab alone group (three [11·5%, 90% CI 1·2-21·8] of 26 patients) and the low-dose radiotherapy group (two [7·7%, 0·0-16·3] of 26 patients; p=0·64) or the hypofractionated radiotherapy group (three [11·5%, 1·2-21·8] of 26 patients; p=0·99). The most common grade 3-4 adverse events were dyspnoea (two [8%] in the durvalumab-tremelimumab alone group; three [12%] in the low-dose radiotherapy group; and three [12%] in the hypofractionated radiotherapy group) and hyponatraemia (one [4%] in the durvalumab-tremelimumab alone group vs two [8%] in the low-dose radiotherapy group vs three [12%] in the hypofractionated radiotherapy group). Treatment-related serious adverse events occurred in one (4%) patient in the durvalumab-tremelimumab alone group (maculopapular rash), five (19%) patients in the low-dose radiotherapy group (abdominal pain, diarrhoea, dyspnoea, hypokalemia, and respiratory failure), and four (15%) patients in the hypofractionated group (adrenal insufficiency, colitis, diarrhoea, and hyponatremia). In the low-dose radiotherapy group, there was one death from respiratory failure potentially related to study therapy. INTERPRETATION: Radiotherapy did not increase responses to combined PD-L1 plus CTLA-4 inhibition in patients with NSCLC resistant to PD(L)-1 therapy. However, PD-L1 plus CTLA-4 therapy could be a treatment option for some patients. Future studies should refine predictive biomarkers in this setting. FUNDING: The US National Institutes of Health and the Dana-Farber Cancer Institute.

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. METHODS AND FINDINGS: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks. CONCLUSIONS: Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.

A roadmap for modelling radiation-induced cardiac disease.

Cardiac risk mitigation is a major priority in improving outcomes for cancer survivors as advances in cancer screening and treatments continue to decrease cancer mortality. More than half of adult cancer patients will be treated with radiotherapy (RT); therefore it is crucial to develop a framework for how to assess and predict radiation-induced cardiac disease (RICD). Historically, RICD was modelled solely using whole heart metrics such as mean heart dose. However, data over the past decade has identified cardiac substructures which outperform whole heart metrics in predicting for significant cardiac events. Additionally, non-RT factors such as pre-existing cardiovascular risk factors and toxicity from other therapies contribute to risk of future cardiac events. In this review, we aim to discuss the current evidence and knowledge gaps in predicting RICD and provide a roadmap for the development of comprehensive models based on three interrelated components, (1) baseline CV risk assessment, (2) cardiac substructure radiation dosimetry linked with cardiac-specific outcomes and (3) novel biomarker development.

Widening the therapeutic window for central and ultra-central thoracic oligometastatic disease with stereotactic MR-guided adaptive radiation therapy (SMART).

BACKGROUND/PURPOSE: Central/ultra-central thoracic tumors are challenging to treat with stereotactic radiotherapy due potential high-grade toxicity. Stereotactic MR-guided adaptive radiation therapy (SMART) may improve the therapeutic window through motion control with breath-hold gating and real-time MR-imaging as well as the option for daily online adaptive replanning to account for changes in target and/or organ-at-risk (OAR) location. MATERIALS/METHODS: 26 central (19 ultra-central) thoracic oligoprogressive/oligometastatic tumors treated with isotoxic (OAR constraints-driven) 5-fraction SMART (median 50 Gy, range 35-60) between 10/2019-10/2022 were reviewed. Central tumor was defined as tumor within or touching 2 cm around proximal tracheobronchial tree (PBT) or adjacent to mediastinal/pericardial pleura. Ultra-central was defined as tumor abutting the PBT, esophagus, or great vessel. Hard OAR constraints observed were ≤ 0.03 cc for PBT V40, great vessel V52.5, and esophagus V35. Local failure was defined as tumor progression/recurrence within the planning target volume. RESULTS: Tumor abutted the PBT in 31 %, esophagus in 31 %, great vessel in 65 %, and heart in 42 % of cases. 96 % of fractions were treated with reoptimized plan, necessary to meet OAR constraints (80 %) and/or target coverage (20 %). Median follow-up was 19 months (27 months among surviving patients). Local control (LC) was 96 % at 1-year and 90 % at 2-years (total 2/26 local failure). 23 % had G2 acute toxicities (esophagitis, dysphagia, anorexia, nausea) and one (4 %) had G3 acute radiation dermatitis. There were no G4-5 acute toxicities. There was no symptomatic pneumonitis and no G2 + late toxicities. CONCLUSION: Isotoxic 5-fraction SMART resulted in high rates of LC and minimal toxicity. This approach may widen the therapeutic window for high-risk oligoprogressive/oligometastatic thoracic tumors.

Foundation model for cancer imaging biomarkers.

Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

Feasibility of Left Anterior Descending Coronary Artery Sparing Radiation Therapy for Locally Advanced Lung Cancer.

Efforts to mitigate radiation therapy (RT)-associated cardiotoxicity have focused on constraining mean heart dose. However, recent studies have shown greater predictive power with cardiac substructure dose metrics, such as the left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15Gy) ≥10%. Herein, we investigated the feasibility of LAD radiation sparing in contemporary intensity modulated RT (IMRT)/volumetric modulated arc therapy (VMAT) lung cancer plans. Single institution retrospective analysis of 54 patients with locally advanced lung cancer treated with thoracic RT was conducted between February 2018 and August 2021. After excluding 33 (5 = non-IMRT/VMAT or intentionally LAD-optimized; 28 = LAD V15Gy <10%), 21 plans with LAD V15Gy ≥10% were identified for LAD reoptimization with intent to meet LAD V15Gy <10% while maintaining meeting organ at risk (OAR) metrics and target coverage with original plan parameters. Dosimetric variables were compared using paired t tests. Most patients (57.1%, 12/21) were treated with definitive RT, 8 of 21 patients (38.1%) with postoperative RT, and 1 with neoadjuvant RT. The median prescribed RT dose was 60 Gy (range, 50.4-66 Gy) in 30 fractions (range, 28-33 fractions). LAD reoptimized plans (vs original) led to significant reductions in mean LAD V15Gy (39.4% ± 13.9% vs 9.4% ± 13.0%; P < .001) and mean LAD dose (12.9 Gy ± 4.6 Gy vs 7.6 Gy ± 2.8 Gy; P < .001). Most (85.7%; 18/21) LAD reoptimized plans achieved LAD V15Gy <10%. There were no statistically significant differences in overall lung, esophageal, or spinal cord dose metrics. Only 1 reoptimization (1/21) exceeded an OAR constraint that was initially met in the original plan. To our knowledge, this is the first report describing the feasibility of LAD-optimized lung cancer RT planning using the newly identified LAD V15Gy constraint. We observed that LAD V15Gy <10% is achievable in more than 85% of plans initially exceeding this constraint, with minimal dosimetric tradeoffs. Our results support the feasibility of routine incorporation of the LAD as an OAR in modern thoracic IMRT/VMAT planning.

Lung sparing in MR-guided non-adaptive SBRT treatment of peripheral lung tumors.

Objective.We aim to: (1) quantify the benefits of lung sparing using non-adaptive magnetic resonance guided stereotactic body radiotherapy (MRgSBRT) with advanced motion management for peripheral lung cancers compared to conventional x-ray guided SBRT (ConvSBRT); (2) establish a practical decision-making guidance metric to assist a clinician in selecting the appropriate treatment modality.Approach.Eleven patients with peripheral lung cancer who underwent breath-hold, gated MRgSBRT on an MR-guided linear accelerator (MR linac) were studied. Four-dimensional computed tomography (4DCT)-based retrospective planning using an internal target volume (ITV) was performed to simulate ConvSBRT, which were evaluated against the original MRgSBRT plans. Metrics analyzed included planning target volume (PTV) coverage, various lung metrics and the generalized equivalent unform dose (gEUD). A dosimetric predictor for achievable lung metrics was derived to assist future patient triage across modalities.Main results.PTV coverage was high (median V100% > 98%) and comparable for both modalities. MRgSBRT had significantly lower lung doses as measured by V20 (median 3.2% versus 4.2%), mean lung dose (median 3.3 Gy versus 3.8 Gy) and gEUD. Breath-hold, gated MRgSBRT resulted in an average reduction of 47% in PTV volume and an average increase of 19% in lung volume. Strong correlation existed between lung metrics and the ratio of PTV to lung volumes (RPTV/Lungs) for both modalities, indicating that RPTV/Lungsmay serve as a good predictor for achievable lung metrics without the need for pre-planning. A threshold value of RPTV/Lungs< 0.035 is suggested to achieve V20 < 10% using ConvSBRT. MRgSBRT should otherwise be considered if the threshold cannot be met.Significance.The benefits of lung sparing using MRgSBRT were quantified for peripheral lung tumors; RPTV/Lungswas found to be an effective predictor for achievable lung metrics across modalities. RPTV/Lungscan assist a clinician in selecting the appropriate modality without the need for labor-intensive pre-planning, which has significant practical benefit for a busy clinic.

Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy.

Background and purpose: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes. Materials and methods: A retrospective study of 47 MR-guided lung SBRT treatments for 39 patients was conducted. Radiomic features were extracted using Pyradiomics, and stability was evaluated temporally and spatially. Delta radiomics were correlated with radiation dose delivery and assessed for associations with tumor control and survival with Cox regressions. Results: Among 107 features, 49 demonstrated temporal stability, and 57 showed spatial stability. Fifteen stable and non-collinear features were analyzed. Median Skewness and surface to volume ratio decreased with radiation dose fraction delivery, while coarseness and 90th percentile values increased. Skewness had the largest relative median absolute changes (22 %-45 %) per fraction from baseline and was associated with locoregional failure (p = 0.012) by analysis of covariance. Skewness, Elongation, and Flatness were significantly associated with local recurrence-free survival, while tumor diameter and volume were not. Conclusions: Our study establishes the feasibility and stability of delta radiomics analysis for MR-guided lung SBRT. Findings suggest that MR delta radiomics can capture short-term radiographic manifestations of the intra-tumoral radiation effect.

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency. This study includes a real-world cohort of 394 patients who each received radiation therapy (mainly for lung cancer). Segmentation of the esophagus was performed by 8 physicians as part of routine clinical care. We evaluated manual segmentation by comparing the length and edge roughness of segmentations among physicians to analyze inconsistencies. We trained eight multiple- and individual-physician segmentation models in total, based on U-Net architectures and residual backbones. We used the volumetric Dice coefficient to measure the performance for each model. We proposed a metric, edge roughness, to quantify the shift of segmentation among adjacent slices by calculating the curvature of edges of the 2D sagittal- and coronal-view projections. The auto-segmentation model trained on multiple physicians (MD1-7) achieved the highest mean Dice of 73.7 ± 14.8%. The individual-physician model (MD7) with the highest edge roughness (mean ± SD: 0.106 ± 0.016) demonstrated significantly lower volumetric Dice for test cases compared with other individual models (MD7: 58.5 ± 15.8%, MD6: 67.1 ± 16.8%, p < 0.001). A multiple-physician model trained after removing the MD7 data resulted in fewer outliers (e.g., Dice ≤ 40%: 4 cases for MD1-6, 7 cases for MD1-7, Ntotal = 394). While we initially detected this pattern in a single clinician, we validated the edge roughness metric across the entire dataset. The model trained with the lowest-quantile edge roughness (MDER-Q1, Ntrain = 62) achieved significantly higher Dice (Ntest = 270) than the model trained with the highest-quantile ones (MDER-Q4, Ntrain = 62) (MDER-Q1: 67.8 ± 14.8%, MDER-Q4: 62.8 ± 15.7%, p < 0.001). This study demonstrates that there is significant variation in style and quality in manual segmentations in clinical care, and that training AI auto-segmentation algorithms from real-world, clinical datasets may result in unexpectedly under-performing algorithms with the inclusion of outliers. Importantly, this study provides a novel evaluation metric, edge roughness, to quantify physician variation in segmentation which will allow developers to filter clinical training data to optimize model performance.

End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

Cardiac Substructure Radiation Dose and Associations With Tachyarrhythmia and Bradyarrhythmia After Lung Cancer Radiotherapy.

Background: Arrhythmias are common following radiotherapy for non-small cell lung cancer. Objectives: The aim of this study was to analyze the association of distinct arrhythmia classes with cardiac substructure radiotherapy dose. Methods: A retrospective analysis was conducted of 748 patients with locally advanced non-small cell lung cancer treated with radiotherapy. Cardiac substructure dose parameters were calculated. Receiver-operating characteristic curve analyses for predictors of Common Terminology Criteria for Adverse Events grade ≥3 atrial fibrillation (AF), atrial flutter, non-AF and non-atrial flutter supraventricular tachyarrhythmia (SVT), bradyarrhythmia, and ventricular tachyarrhythmia (VT) or asystole were calculated. Fine-Gray regression models were performed (with noncardiac death as a competing risk). Results: Of 748 patients, 128 (17.1%) experienced at least 1 grade ≥3 arrhythmia, with a median time to first arrhythmia of 2.0 years (Q1-Q3: 0.9-4.2 years). The 2-year cumulative incidences of each arrhythmia group were 8.0% for AF, 2.7% for atrial flutter, 1.8% for other SVT, 1.4% for bradyarrhythmia, and 1.1% for VT or asystole. Adjusting for baseline cardiovascular risk, pulmonary vein (PV) volume receiving 5 Gy was associated with AF (subdistribution HR [sHR]: 1.04/mL; 95% CI: 1.01-1.08; P = 0.016), left circumflex coronary artery volume receiving 35 Gy with atrial flutter (sHR: 1.10/mL; 95% CI: 1.01-1.19; P = 0.028), PV volume receiving 55 Gy with SVT (sHR: 1.03 per 1%; 95% CI: 1.02-1.05; P < 0.001), right coronary artery volume receiving 25 Gy with bradyarrhythmia (sHR: 1.14/mL; 95% CI: 1.00-1.30; P = 0.042), and left main coronary artery volume receiving 5 Gy with VT or asystole (sHR: 2.45/mL; 95% CI: 1.21-4.97; P = 0.013). Conclusions: This study revealed pathophysiologically distinct arrhythmia classes associated with radiotherapy dose to discrete cardiac substructures, including PV dose with AF and SVT, left circumflex coronary artery dose with atrial flutter, right coronary artery dose with bradyarrhythmia, and left main coronary artery dose with VT or asystole, guiding potential risk mitigation approaches.

Stereotactic Magnetic Resonance-guided Adaptive Radiation Therapy for Localized Kidney Cancer: Early Outcomes from a Prospective Phase 1 Trial and Supplemental Cohort.

Stereotactic magnetic resonance (MR)-guided adaptive radiotherapy (SMART) for renal cell carcinoma may result in more precise treatment delivery through the capabilities for improved image quality, daily adaptive planning, and accounting for respiratory motion during treatment with real-time MR tracking. In this study, we aimed to characterize the safety and feasibility of SMART for localized kidney cancer. Twenty patients with localized kidney cancer (ten treated in a prospective phase 1 trial and ten in the supplemental cohort) were treated to 40 Gy in five fractions on a 0.35 T MR-guided linear accelerator with daily adaptive planning and a cine MR-guided inspiratory breath hold technique. The median follow-up time was 17 mo (interquartile range: 13-20 months). A single patient developed local failure at 30 mo. No grade ≥3 adverse events were reported. The mean decrease in estimated glomerular filtration rate was -1.8 ml/min/1.73 m2 (95% confidence interval or CI [-6.6 to 3.1 ml/min/1.73 m2]), and the mean decrease in tumor diameter was -0.20 cm (95% CI [-0.6 to 0.2 cm]) at the last follow-up. Anterior location and overlap of the 25 or 28 Gy isodose line with gastrointestinal organs at risk were predictive of the benefit from online adaptive planning. Kidney SMART is feasible and, at the early time point evaluated in this study, was well tolerated with minimal decline in renal function. More studies are warranted to further evaluate the safety and efficacy of this technique. PATIENT SUMMARY: For patients with localized renal cell carcinoma who are not surgical candidates, stereotactic magnetic resonance--guided adaptive radiotherapy is a feasible and safe noninvasive treatment option that results in minimal impact on kidney function.

Large language models to identify social determinants of health in electronic health records.

Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. However, class imbalance and data limitations present challenges for this sparsely documented yet critical information. Here, we investigated the optimal methods for using LLMs to extract six SDoH categories from narrative text in the EHR: employment, housing, transportation, parental status, relationship, and social support. The best-performing models were fine-tuned Flan-T5 XL for any SDoH mentions (macro-F1 0.71), and Flan-T5 XXL for adverse SDoH mentions (macro-F1 0.70). Adding LLM-generated synthetic data to training varied across models and architecture, but improved the performance of smaller Flan-T5 models (delta F1 + 0.12 to +0.23). Our best-fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models in the zero- and few-shot setting, except GPT4 with 10-shot prompting for adverse SDoH. Fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p < 0.05). Our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. These results demonstrate the potential of LLMs in improving real-world evidence on SDoH and assisting in identifying patients who could benefit from resource support.

Body Composition in Advanced Non-Small Cell Lung Cancer Treated With Immunotherapy.

Importance: The association between body composition (BC) and cancer outcomes is complex and incompletely understood. Previous research in non-small-cell lung cancer (NSCLC) has been limited to small, single-institution studies and yielded promising, albeit heterogeneous, results. Objectives: To evaluate the association of BC with oncologic outcomes in patients receiving immunotherapy for advanced or metastatic NSCLC. Design, Setting, and Participants: This comprehensive multicohort analysis included clinical data from cohorts receiving treatment at the Dana-Farber Brigham Cancer Center (DFBCC) who received immunotherapy given alone or in combination with chemotherapy and prospectively collected data from the phase 1/2 Study 1108 and the chemotherapy arm of the phase 3 MYSTIC trial. Baseline and follow-up computed tomography (CT) scans were collected and analyzed using deep neural networks for automatic L3 slice selection and body compartment segmentation (skeletal muscle [SM], subcutaneous adipose tissue [SAT], and visceral adipose tissue). Outcomes were compared based on baseline BC measures or their change at the first follow-up scan. The data were analyzed between July 2022 and April 2023. Main Outcomes and Measures: Hazard ratios (HRs) for the association of BC measurements with overall survival (OS) and progression-free survival (PFS). Results: A total of 1791 patients (878 women [49%]) with NSCLC were analyzed, of whom 487 (27.2%) received chemoimmunotherapy at DFBCC (DFBCC-CIO), 825 (46.1%) received ICI monotherapy at DFBCC (DFBCC-IO), 222 (12.4%) were treated with durvalumab monotherapy on Study 1108, and 257 (14.3%) were treated with chemotherapy on MYSTIC; median (IQR) ages were 65 (58-74), 66 (57-71), 65 (26-87), and 63 (30-84) years, respectively. A loss in SM mass, as indicated by a change in the L3 SM area, was associated with worse oncologic outcome across patient groups (HR, 0.59 [95% CI, 0.43-0.81] and 0.61 [95% CI, 0.47-0.79] for OS and PFS, respectively, in DFBCC-CIO; HR, 0.74 [95% CI, 0.60-0.91] for OS in DFBCC-IO; HR, 0.46 [95% CI, 0.33-0.64] and 0.47 [95% CI, 0.34-0.64] for OS and PFS, respectively, in Study 1108; HR, 0.76 [95% CI, 0.61-0.96] for PFS in the MYSTIC trial). This association was most prominent among male patients, with a nonsignificant association among female patients in the MYSTIC trial and DFBCC-CIO cohorts on Kaplan-Meier analysis. An increase of more than 5% in SAT density, as quantified by the average CT attenuation in Hounsfield units of the SAT compartment, was associated with poorer OS in 3 patient cohorts (HR, 0.61 [95% CI, 0.43-0.86] for DFBCC-CIO; HR, 0.62 [95% CI, 0.49-0.79] for DFBCC-IO; and HR, 0.56 [95% CI, 0.40-0.77] for Study 1108). The change in SAT density was also associated with PFS for DFBCC-CIO (HR, 0.73; 95% CI, 0.54-0.97). This was primarily observed in female patients on Kaplan-Meier analysis. Conclusions and Relevance: The results of this multicohort study suggest that loss in SM mass during systemic therapy for NSCLC is a marker of poor outcomes, especially in male patients. SAT density changes are also associated with prognosis, particularly in female patients. Automated CT-derived BC measurements should be considered in determining NSCLC prognosis.