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PURPOSE: Patients with metastatic cancer benefit from advance care planning (ACP) conversations. We aimed to improve ACP using a computer model to select high-risk patients, with shorter predicted survival, for conversations with providers and lay care coaches. Outcomes included ACP documentation frequency and end-of-life quality measures. METHODS: In this study of a quality improvement initiative, providers in four medical oncology clinics received Serious Illness Care Program training. Two clinics (thoracic/genitourinary) participated in an intervention, and two (cutaneous/sarcoma) served as controls. ACP conversations were documented in a centralized form in the electronic medical record. In the intervention, providers and care coaches received weekly e-mails highlighting upcoming clinic patients with < 2 year computer-predicted survival and no prior prognosis documentation. Care coaches contacted these patients for an ACP conversation (excluding prognosis). Providers were asked to discuss and document prognosis. RESULTS: In the four clinics, 4,968 clinic visits by 1,251 patients met inclusion criteria (metastatic cancer with no prognosis previously documented). In their first visit, 28% of patients were high-risk (< 2 year predicted survival). Preintervention, 3% of both intervention and control clinic patients had ACP documentation during a visit. By intervention end (February 2021), 35% of intervention clinic patients had ACP documentation compared with 3% of control clinic patients. Providers' prognosis documentation rate also increased in intervention clinics after the intervention (2%-27% in intervention clinics, P < .0001; 0%-1% in control clinics). End-of-life care intensity was similar in intervention versus control clinics, but patients with ≥ 1 provider ACP edit met fewer high-intensity care measures ( P = .04). CONCLUSION: Combining a computer prognosis model with care coaches increased ACP documentation.

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PURPOSE Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer. METHODS We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured ( bag-of-words, doc2vec, fasttext), and combinations of both ( structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes. RESULTS For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types. CONCLUSION Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.

Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for the prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumour histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumour morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumour subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumour-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumour segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumour classification may inform prognosis and treatment response for precision medicine. Radiomics has been used to discover imaging signatures that predict therapy response and outcomes, but clinical translation has been slow. Using machine learning methods, the authors report tumour subtypes that are applicable across major imaging modalities and three cancer types. The tumour subtypes have distinct radiological and molecular features, as well as survival outcomes after conventional therapies.

To quantify the survival impact of prolongation of definitive radiotherapy (RT) for head and neck cancer in a national, modern cohort, and to identify predictive factors for prolongation.The National Cancer Database was queried for adults with non-metastatic cancer of the nasopharynx, oropharynx, larynx, or hypopharynx diagnosed 2004-2015, treated with definitive RT to 66-70 Gy in 30-35 fractions at 2-2.2 Gy per fraction. Multivariable Cox regression and propensity score matching were used to model the survival impact of RT prolongation, adjusting for potential confounders such as age and comorbidity. Predictors of RT prolongation were identified using multivariable multinomial logistic regression.In total, 36,367 patients were identified. As a continuous variable, RT prolongation increased the relative hazard of death by 2% per day (P .0001). In the matched cohorts, patients with short (4-8 days) or long prolongation (8 days) had lower absolute 4-year overall survival by 4% and 12%, respectively (P .0001), while prolongation of 1-3 days was not significantly adverse. Major predictors of increased risk of prolongation were administration of systemic therapy, baseline comorbidity, lack of private insurance, and tumor/nodal stage. Conversely, higher facility volume was significantly protective, with a 55% lower risk of long prolongation within the topmost quartile (11.5 patients/year).RT prolongation, especially8 days, is significantly deleterious. Systemic therapy and facility volume were major predictors. Early identification of patients at increased risk of treatment interruptions may facilitate implementation of preventive measures.

Programmed death-1 immune checkpoint blockade improves survival of patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but the benefits of addition to (chemo)radiation for newly diagnosed patients with HNSCC remain unknown.We evaluated the safety of nivolumab concomitant with 70 Gy intensity modulated radiation therapy and weekly cisplatin (arm 1), every 3-week cisplatin (arm 2), cetuximab (arm 3), or alone for platinum-ineligible patients (arm 4) in newly diagnosed intermediate- or high-risk locoregionally advanced HNSCC. Patients received nivolumab from 2 weeks prior to radiation therapy until 3 months post-radiation therapy. The primary endpoint was dose-limiting toxicity (DLT). If ≤2 of the first 8 evaluable patients experienced a DLT, an arm was considered safe. Secondary endpoints included toxicity and feasibility of adjuvant nivolumab to 1 year, defined as all 7 additional doses received by ≥4 of the first 8 evaluable patients across arms.Of 39 patients (10 in arms 1, 3, 4 and 9 in arm 2), 72% had T3-4 tumors, 85% had N2-3 nodal disease, and 67% had10 pack-years of smoking. There were no DLTs in arms 1 and 2, 1 in arm 3 (mucositis), and 2 in arm 4 (lipase elevation and mucositis in 1 and fatigue in another). The most common grade ≥3 nivolumab-related adverse events were lipase increase, mucositis, diarrhea, lymphopenia, hyponatremia, leukopenia, fatigue, and serum amylase increase. Adjuvant nivolumab was feasible as defined in the protocol.Concomitant nivolumab with the 4 tested regimens was safe for patients with intermediate- and high-risk HNSCC, and subsequent adjuvant nivolumab was feasible as defined (NCT02764593).

Metabolic response assessment for oropharyngeal squamous cell carcinoma (OPSCC) aids in identifying locoregional persistence/recurrence (LRR). The Hopkins Criteria are a standardized qualitative response assessment system using posttreatment FDG-PET/CT.We conducted a retrospective cohort study of patients with node-positive OPSCC treated with definitive (chemo)radiotherapy. We assessed Hopkins Criteria performance for LRR, then developed and validated a competing-risks model.Between 2004 and 2018, 259 patients were included with median follow-up of 43 months. The Hopkins Criteria sensitivity, specificity, negative predictive value, and accuracy were 68%, 88%, 95%, and 85%. The 36-month cumulative incidence of LRR was greater with positive scores (45% vs. 5%, HR 12.60, p 0.001). PET/CTs performed ≤10 weeks after radiotherapy were associated with a four-fold increase in pathologically negative biopsies/surgeries (36% vs. 9%, p = 0.03). The AUC for LRR was 0.89 using a model integrating the Hopkins score.The Hopkins Criteria predict LRR with high accuracy for OPSCC response assessment.

PURPOSE For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data. MATERIALS AND METHODS Patients diagnosed with metastatic cancer from 2008 to 2014 were included in this retrospective study. Patients who were diagnosed with cancer or received their initial treatment within our system were included in the institutional cancer registry and this study. Overall survival was calculated using the Kaplan-Meier method. Survival curves were generated in two ways: using EMR follow-up data alone and using EMR data supplemented with data from the Stanford Cancer Registry/California Cancer Registry. RESULTS Four thousand seventy-seven patients were included. The median follow-up using EMR + Cancer Registry data was 19.9 months, and the median follow-up in surviving patients was 67.6 months. There were 1,301 deaths recorded in the EMR and 3,140 deaths recorded in the Cancer Registry. The median overall survival from the date of cancer diagnosis using EMR data was 58.7 months (95% CI, 54.2 to 63.2); using EMR + Cancer Registry data, it was 20.8 months (95% CI, 19.6 to 22.3). A similar pattern was seen using the date of first systemic therapy or date of first hospital admission as the baseline date. CONCLUSION Using EMR data alone, survival time was overestimated compared with EMR + Cancer Registry data.

Objectives To investigate the benefit of postoperative radiotherapy (PORT) for low-volume (pN1) nodal disease after resection of oral cavity squamous cell carcinoma. Materials and methods The National Cancer Database was queried for adults with nonmetastatic squamous cell carcinoma of the oral cavity treated by surgical resection with pathologic stage T1-2 N0-2 (American Joint Committee on Cancer 7th edition) and with the maximal exclusion of standard indications for PORT. Overall survival was compared within pN1 for observation versus PORT and then compared for pN1 versus pN0 and versus pN2 stratified by receipt of observation or PORT. Multivariable Cox regression was used to adjust for potential confounders between PORT and survival, including comorbidity and age. Results Overall 5017 pN0, 530 pN1, and 253 pN2 patients were identified, of whom 9%, 35%, and 64% received PORT, respectively. Within the pN1 cohort, PORT was associated with improved survival versus observation (adjusted hazard ratio, 0.66; 95% confidence interval, 0.46-0.97; P=0.03). Among observed patients, the prognosis of pN1 was equivalent to pN2 and inferior to pN0; in contrast, among patients treated with PORT, the prognosis of pN1 was equivalent to pN0 and superior to pN2. Without PORT, pN1 remained an adverse risk factor relative to pN0 regardless of the depth of invasion, lymph node size, lymph node location, and extent of lymph node dissection. Conclusions PORT was associated with a survival benefit compared with observation. Notably, pN1 was an adverse risk factor relative to pN0 if, and only if, patients did not receive PORT, suggesting pN1 by itself may be an indication for PORT.

Objective Being able to predict a patient’s life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians’ performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model. Materials and Methods A machine learning model was trained using 14 600 metastatic cancer patients’ data to predict each patient’s distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015–2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots. Results The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63–0.81), 0.77 (0.73–0.81), and 0.68 (0.65–0.71), respectively. Conclusions The machine learning model’s predictions were more accurate than those of the treating physician or a traditional model.

PURPOSE The aim of this study was to report local failure (LF) outcomes and associated predictors in patients with oligometastatic colorectal cancer (CRC) treated with stereotactic ablative radiotherapy (SABR). MATERIALS AND METHODS We retrospectively reviewed patients with CRC metastases to the brain, liver, spine, or lung treated with SABR between 2001 and 2016. Time to LF was summarized using cumulative incidence of LF curves with death as a competing risk. RESULTS The analysis included a total of 130 patients and 256 lesions. Of the metastases treated, 129 (50%) were brain, 50 (20%) liver, 49 (19%) spine, and 28 (11%) lung. Median gross tumor volume was 24 mL for liver metastases, 2 mL for brain metastases, 4 mL for spine metastases, and 1 mL for lung metastases. The overall 1, 2, and 3-year cumulative incidence of LF rates were 21.6% (16.5, 27.1), 28.2% (22.3, 34.4), and 31.5% (25.2, 38.0), respectively. LF was highest among the liver metastases (1 y: 26.0%, 2 y: 38.5%), followed by spine (1 y: 25.1%, 2 y: 31.1%), brain (1 y: 20%, 2 y: 25.2%), and lung (1 y: 13.7%, 2 y: insufficient data). Metastases from right-sided primary CRC were significantly more likely to have LF (P=0.0146, HR=2.23). Biologically effective dose>70 Gy, defined using a standard linear quadratic model using α/β ratio of 10 on the individual lesion level, and pre-SABR chemotherapy were also significant predictors of LF (P= 0.0009 and 0.018, respectively). CONCLUSIONS CRC metastases treated with SABR had significantly higher rates of LF if they originated from right-sided primary CRC, compared with left-sided. Liver metastases had the highest rates of LF compared with other metastatic sites. Thus, CRC liver metastases and metastases from right-sided CRC may benefit from more aggressive radiotherapy.

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on (18)F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4–13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7–12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

Background Immune checkpoint inhibitors (ICIs) and stereotactic radiosurgery (SRS) are increasingly used together to manage brain metastases (BMs). We assessed adverse radiation effect, disease control, and overall survival in patients with BMs who received SRS with anticytotoxic T-lymphocyte–associated protein 4 and/or anti– programmed cell death protein receptor/ligand therapies. Methods We retrospectively reviewed the records of patients with intact or resected BMs treated with SRS and ICIs within 5 months of SRS between 2010 and 2018. Patients were defined as receiving concurrent SRS and ICI if a dose of ICI was given within 4 weeks of SRS. Local failure, distant intracranial failure, extracranial failure, and adverse radiation effect were assessed using cumulative incidence rates and competing risk regressions with death as a competing risk. Overall survival was assessed using the Kaplan-Meier method and Cox proportional hazards models. Results A total of 97 patients with 580 BMs were included in our analysis. Competing risk analyses showed that concurrent SRS-ICI therapy is associated with higher rates of adverse radiation effect (6.4% vs. 2.0% at 1 year; multivariable hazard ratio [HR], 4.47; 95% confidence interval [CI], 1.57–12.73; P = 0.005), lower rates of extracranial failure (69.7% vs. 80.8% at 1 year; multivariable HR, 0.60; 95% CI, 0.42–0.87; P = 0.007), and better overall survival (48.6% vs. 25.4% at 1 year; multivariable HR, 0.57; 95% CI, 0.33–0.99; P = 0.044) compared with nonconcurrent therapy. SRS-ICI timing was not associated with local failure or distant intracranial failure. Conclusions Concurrent SRS-ICI therapy has a tolerable adverse event profile and may improve extracranial disease control and overall survival, supporting concurrent use in the management of BMs.

Our purpose was to evaluate the incidence of acute and late esophageal toxicity in patients with thoracic tumors near or abutting the esophagus treated with SABR.Among patients with thoracic tumors treated with SABR, we identified those with tumors near or abutting the esophagus. Using the linear-quadratic model with an α/ß ratio of 10, we determined the correlation between dosimetric parameters and esophageal toxicity graded using the Common Terminology Criteria for Adverse Events, version 5.0.Out of 2200 patients treated with thoracic SABR, 767 patients were analyzable for esophageal dosimetry. We identified 55 patients with tumors near the esophagus (52 evaluable for esophagitis grade) and 28 with planning target volume (PTV) overlapping the esophagus. Dose gradients across the esophagus were consistently sharp. Median follow-up and overall survival were 16 and 23 months, respectively. Thirteen patients (25%) developed temporary grade 2 acute esophageal toxicity, 11 (85%) of whom had PTV overlapping the esophagus. Symptoms resolved within 1 to 3 months in 12 patients and 6 months in all patients. No grade 3 to 5 toxicity was observed. Only 3 patients (6%) developed late or persistent grade 2 dysphagia or dyspepsia of uncertain relationship to SABR. The cumulative incidence of acute esophagitis was 15% and 25% at 14 and 60 days, respectively. Acute toxicity correlated on univariate analysis with esophageal DAlthough 25% of patients with tumors near the esophagus developed acute esophagitis (39% of those with PTV overlapping the esophagus), these toxicities were all grade 2 and all temporary. This suggests the safety and efficacy of thoracic SABR for tumors near or abutting the esophagus when treating with high conformity and sharp dose gradients.

Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.

e13515 Background: Use of systemic anti-cancer treatment near the end of life (EOL) is recognized as a low value practice with limited benefit to patients. Machine learning (ML) models that identify patients in close proximity to death can help prospectively assess oncology practice of systemic therapy use. We hypothesized that systemic therapy use would be higher based on predicted survival compared with actual survival. Methods: We calculated prevalence of systemic therapy use based on predicted and actual survival among patients with metastatic cancer at Stanford Healthcare from 2008-2019. Patients were included if they were in the test set of the ML model, had an eligible outpatient oncology clinic visit for which a predicted survival was calculated and were deceased . Median predicted survival was calculated from the ML model at each outpatient oncology visit and treatment was linked to a visit date if within 14 days of each other. Prevalence of systemic therapy was calculated for patients with a predicted or actual survival of < 6 months, 6-12 months, 12-18 months and 18-24 months. The five categories of treatment were: chemotherapy, targeted/antibody, hormone, immunotherapy, and other. Results: A total of 951 deceased patients who received anticancer treatment are included and a total of 21,283 doses of treatment were administered with a mean of 22 doses per patient. The median age at metastatic cancer diagnosis was 58 years, 53% of patients were female and most patients identified as White (55%) or Asian (23%). The most common disease groups were gastrointestinal (21.6%), thoracic (18.6%) and breast (14.9%). Overall, the use of different treatment types did not differ based on either predicted or actual survival (Table). In all the survival groupings, chemotherapy remained the predominant medication type, however with a trend of decreasing use with longer predicted and actual survival. Conclusions: The use of cancer medications and the type of medication given did not change based on predicted or actual survival in a large group of patients with metastatic cancer. There was a trend of decreasing chemotherapy use with longer prognosis. Further investigation into use in time intervals closer to (predicted or actual) death and inclusion of those who did not receive any systemic therapy are underway.[Table: see text]

PURPOSE/OBJECTIVE(S) Rapid and accurate estimation of tumor burden in biomedical images is essential for precisely monitoring cancer progression and assessing therapeutic response. The ability to detect and segment tumors using an automated approach is a key part of this task. Despite recent advances from deep learning, lung tumor delineation remains challenging, particularly when the tumor bounding box is not provided to the model. We hypothesized that clinical radiation oncology contours could supply a large enough dataset of 3D tumor segmentations to enable more accurate models. We developed and validated a deep learning-based model to identify and segment primary and metastatic lung tumors on computed tomography (CT) images. MATERIALS/METHODS We curated a dataset consisting of CT images and clinical segmentations of 1,916 lung tumors in 1,504 patients who received radiation treatment for one or more primary or metastatic lung tumors. Segmentation quality was independently verified by a radiation oncologist using a custom web application. This dataset was used to train two 3D U-Net convolutional neural networks with varying model properties: one using high-resolution and small input volumes, and one using low-resolution and large input volumes. Models were ensembled together during validation. Performance was evaluated using an external held-out test set of CT images and segmentations from 59 patients with a single primary or metastatic lung tumor, treated at a separate clinical site. This test set consisted of 50 primary lung cancers and 9 metastases. To benchmark model performance against physicians, the test set was also contoured by two additional radiation oncologists. RESULTS Median tumor volume in the external test set was 80.48 cubic centimeters (interquartile range [IQR]: 14.40 to 177.65). The segmentations generated by the ensembled model produced a mean Dice coefficient of 0.62 (IQR: 0.47 to 0.85) on the test set. The sensitivity for detecting a tumor, as defined by correctly predicting at least one voxel within a ground truth tumor, was 93.2%, and the Dice coefficient for the scans with correctly identified lesions was 0.67 (IQR: 0.53 to 0.85). In comparison, the mean interobserver Dice coefficient for the three physicians on the test set was 0.76 (IQR: 0.70 to 0.84). We observed strong correlation between physician-determined tumor size and model-predicted tumor size (Pearson correlation, r = 0.69, P < 0.0001). CONCLUSION An end-to-end deep learning-based model was able to identify and segment lung tumors in a completely automated fashion, with near-expert level performance. Such models could soon be useful for clinical contouring and automatic quantification of tumor burden.

PURPOSE/OBJECTIVE(S) The first clinical biology-guided radiation therapy (BgRT) system - RefleXion X1 - is installed and is being commissioned for clinical use at our institution. The system delivers 6MV-FFF beam via 50 firing positions at 850 cGy/min dose rate on 60 rpm rotating gantry with the couch advancing in 2.1 mm increments. Collimation is achieved through a binary multi-leaf collimator system composed of 64 (6.25 mm width at 850 cm SAD) leaves and 10mm or 20mm jaws. The purpose of this study was to compare the treatment plan quality and delivery efficiency for SBRT cases without PET-guidance. MATERIALS/METHODS Five lung cancer patients (50 Gy/4fx) with motion-inclusive PTV previously treated with VMAT SBRT were selected for this retrospective study. For each VMAT plan a corresponding plan was generated on the RefleXion treatment planning system (TPS) using our institutional planning constraints. VMAT SBRT plans used 1-2 partial arcs with 10MV-FFF energy and 2400 MU/min dose rate. RefleXion SBRT plans used all 50 firing positions and 1 cm jaw collimation. As plan normalization option does not yet exist in the RefleXion TPS, the plans were optimized to achieve PTV D95% coverage with 100% of prescription dose to the best of planner's ability. All clinically relevant metrics in this study, including plan PTV D95%, PTV D1%, 50% isodose volume (I50%), Conformity Index (CI), organs at risk constraints, and treatment time were analyzed and compared between VMAT and RefleXion plans using paired t-tests. RESULTS Clinically acceptable plans were obtained with both techniques. The average PTV volume was 14.1 ± 6.4cc. For VMAT and RefleXion plans, no statistically significant difference was observed in PTV D95%, PTV D1%, CI or I50%. Ipsilateral lung volume spared at 12.4 Gy (VS12.4) was slightly greater (31.6cc increase, P < 0.035) and skin Dmax was slightly lower (1.4 Gy decrease, P < 0.038) for RefleXion plans. Treatment beam-on time was significantly longer for RefleXion plans (23.4min increase, P < 0.0002). CONCLUSION RefleXion TPS provided comparable plan quality to lung SBRT VMAT plans. Treatment beam-on time is significantly longer for the RefleXion system.

Purpose/objective(s) The X1 system ("X1") integrates a fully functional Positron Emission Tomography (PET) combined with a fan-beam kVCT system into the architecture of a ring-gantry linear accelerator (LINAC) for biology-guided radiotherapy (BgRT) delivery. The integration of PET into a LINAC framework required design features that deviate from the diagnostic setting. For instance, the X1 houses two opposed 90° PET arcs that rotate at 60 RPM to generate sinogram reconstructed images rather than a static, circumferential array of detectors. To better understand X1 PET performance, we conducted a side-by-side comparison of images of a PET phantom obtained sequentially on the X1 PET subsystem and a traditional PET/CT scanner. Materials/methods An independent quality assurance phantom utilized a custom insert with two-insert targets filled with 18F-fluorodeoxyglucose (FDG) diluted water. Spherical target had a diameter of 22 mm and C-shape geometry consisted of an active volume of 26mm axial length and 255-degree partial annulus with major and minor radii of 27 mm x 12 mm, respectively. The targets were filled with an activity concentration of 9.3 kBq/ml to achieve a target to background ratio of 8:1 simulating the target uptake with hot background in a patient. The phantom was imaged within 30 minutes of preparation on the X1 PET and then immediately transferred to a PET/CT Biograph scanner for a second image. This experiment was repeated three times in separate days to assess reproducibility of PET acquisition. The PET images were visually assessed by the radiation oncologists to ensure the PET signal is within PTV. The PET signals of the targets were also evaluated using the full-width-half-max (FWHM) calculated on left-right (RL), anterior-posterior (AP) and superior-inferior (SI) directions for both targets and compared to the physical dimensions of the cavities. Results The imaging acquisition tests were successfully completed on all three days for both systems, as per the prescribed workflow. In all experiments, radiation oncologists verified that the PET avid area were within the PTV in both systems. Average FWHM results of the 22 mm spherical target on X1 PET were 24.5, 21.7 and 17.0 mm as compared to 20.0, 20.0 and 18.2 mm for PET/CT in LR, AP and SI directions, respectively. For the C-shape object X1 PET resulted in 18.8, 19.5, 21.8 and 25.0 mm whereas the PET/CT resulted in 12.7, 13.2, 13.2 and 24.7 mm FWHMs for the two RL C-shape legs, AP and SI directions, respectively as compared to 15-, 15-, 15- and 26-mm physical dimensions. Conclusion The X1 PET subsystem - which utilizes dual PET arcs and a sinogram image reconstruction algorithm in order to integrate into a LINAC architecture - generates images that are qualitatively comparable to a traditional PET scanner. In general, X1 FWHM results were slightly larger and PET/CT ones were smaller than the physical dimensions in axial plane. Both systems slightly underestimated the target size in SI direction.

Purpose/Objective(s) Stereotactic ablative radiotherapy (SABR) is an effective treatment for lung tumors, but can result in toxicity such as chest wall pain and life-threatening damage to central lung structures. We hypothesized that while larger tumors require higher dose, small tumors up to 10cc in volume can be controlled with biologically effective dose Materials/Methods Patients in three groups were enrolled: initial diagnosis of non-small cell lung cancer (NSCLC), AJCC 7th edition stage T1-3 N0 M0 (group 1); new primary NSCLC with history of NSCLC, or multiple synchronously diagnosed NSCLCs (group 2); and lung metastases from NSCLC or another primary site (group 3). Up to four tumors could be treated with once-daily SABR. There were six dose/fractionation schedules used, depending on gross tumor volume (≤10cc, 10-30cc, > 30cc) and location (peripheral vs. central). Larger tumors received higher dose and central tumors generally received lower dose per fraction. Dose ranged from 25Gy in one fraction for 0-10cc peripheral tumors to 60Gy in 8 fractions for > 30cc central tumors. Colorectal cancer metastases were treated to higher dose, at least 50Gy in 4 fractions. The primary endpoint was per-group cumulative incidence of local recurrence at 1 year (recurrence of treated tumor within same lobe), with distant recurrence and death as competing risks. Treated tumor recurrence (recurrence with epicenter within 1cm of PTV) and toxicity were also analyzed. Results A total of 217 patients were enrolled from 2011-2018 (some patients were enrolled multiple times). Median age was 72, 59% were male, and 69% were current/former smokers. There were 240 treatment courses and 285 tumors treated (range 1-3 tumors per course). 211 tumors were peripheral and 74 were central. Tumor size distribution was: ≤10cc, 74%; 10-30cc, 19%; > 30cc, 7%. The most common dose was 25Gy in one fraction (158 tumors). Median follow-up was 30 months (range 2-95). Median overall survival was 57 months. Local recurrence data are currently being updated and will be presented at the meeting. The rate of grade 2 or higher pneumonitis was 16/217 (7%) and grade 3 or higher pneumonitis was 3/217 (1%). The rate of grade 2 or higher chest wall pain was 13/217 (6%). One patient had a grade 5 adverse event, developing pulmonary hemorrhage that was possibly related to radiotherapy, 17 months after treatment of a large central NSCLC. Conclusion Individualized SABR to lung cancers resulted in excellent local control and favorable toxicity profile.

Author(s): Gensheimer, Michael F; Yom, Sue S; Soto, Nancy; Dignam, James J; Le, Quynh-Thu; Machtay, Mitchell; Curran, Walter J

Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

Background The present study examined clinical stage I non–small-cell lung cancer (NSCLC) treatment in the population-based California Cancer Registry. Patients and Methods The characteristics associated with first clinical stage I NSCLC treatment (surgery, radiation, no local therapy) from 2003 to 2014 were identified using logistic regression. Survival was evaluated using Kaplan-Meier and Cox proportional hazard analyses. Results Surgery was used in most patients who met the inclusion criteria (14,545 of 19,893; 73.1%), although relatively similar numbers had undergone radiation (n = 2848; 14.3%) or not received therapy (n = 2500; 12.6%). Surgery use ranged from 68.5% to 77.2% patients annually. The percentage of patients with no therapy decreased from 18.1% (315 of 1737) in 2003 to 10.3% (176 of 1703) in 2014, and radiation use increased from 10.7% (185 of 1737) in 2003 to 21.2% (361 of 1703) in 2014. Patients who did not receive therapy were more likely to be older, not white, male, and unmarried, to have no insurance or public insurance other than Medicare, to live in a lower socioeconomic status neighborhood, to have been seen at a non–National Cancer Institute cancer center hospital or hospital serving lower socioeconomic status patients, and to have larger tumors. The 5-year all-cause survival after no therapy (12.7%) was significantly worse than that after surgery (64.9%) or radiation (21.5%; P Conclusion In the present population-based analysis, surgery was the most common treatment for clinical stage I NSCLC but was not used for almost 27% of patients. Radiation use increased and the proportion of patients who did not receive any therapy decreased over time.

Purpose Stereotactic ablative radiation therapy (SABR) to lung tumors close to the chest wall can cause rib fractures or chest wall pain. We evaluated and propose a clinically practical solution of using noncoplanar volumetric modulated arc radiation therapy (VMAT) to reduce chest wall dose from lung SABR. Methods and materials Twenty lung SABR VMAT plans in which the chest wall volume receiving 30 Gy or higher (V30) exceeded 30 mL were replanned by noncoplanar VMAT with opposite 15° couch kicks. Dosimetric parameters including chest wall V30 and V40; lung V5, V10, V20, and mean dose; Paddick high-dose conformity index; intermediate-dose conformity index; and monitor units (MU) for each plan were used to evaluate the plan quality. The treatment time was also estimated by delivering the entire treatment. Two-sided paired t test was used to evaluate the difference of the dosimetric parameters between coplanar 1 arc (cVMAT1), coplanar 2 arcs (cVMAT2), and noncoplanar two arcs (nVMAT2) plans; differences with P Results V30 and V40 for chest wall were reduced on average by 20% ± 9% and 15% ± 11% (mean ± standard deviation) from cVMAT2 plans to nVMAT2 plans (P Conclusions We found that noncoplanar VMAT plans are feasible, clinically practical to deliver, and significantly reduce V30 and V40 of chest wall without increasing lung dose.

Purpose/Objective(s) Contouring organs at risk (OARs) is a key step of radiotherapy treatment planning. In this study, a deep learning-based framework was developed to automatize the whole OAR contouring process contributing to the clinical practice. Materials/Methods This auto-segmentation framework consisted of a pre-processing module, a deep learning-based segmentation module, and a post-processing module. We focused on the auto-segmentation in head and neck, and pelvis sites, and CT images with manual OAR contours from 67 and 54 patient cases were collected from our clinical database for the deep learning, respectively. 16 head and neck OARs and 11 pelvis OARs which were contoured in most patient cases were selected for auto-segmentation. In the pre-processing module, the original image data was extracted from the clinical DICOM Image file, and then processed for the segmentation network learning. A U-Net like network was designed in the auto-segmentation module, which was designed to consider the contour consistency among adjacent image slices and speed up the network convergency. Manual OAR contours were used as the ground truth for the network training, and a weighted Dice loss function was utilized to handle the missing organ contours in some patient cases. The segmentation results would be converted to a DCIOM Structure file in the post-processing module for treatment planning. Results The accuracy of the current auto-segmentation framework in terms of the Dice similarity coefficient (DSC) is shown in the table. Once a clinical DICOM Image data is sent to the framework, a corresponding DICOM Structure file would be automatically generated containing the OAR segmentation results. The overall operation takes about 1-2 min per case without the need for additional manual interference. Conclusion A deep learning-based framework was developed for the auto-segmentation of head and neck, and pelvis OARs with promising contouring accuracy and operation time, which is now ready to be applied in our clinical practice. Moreover, this framework is being developed toward a physician-specific framework to comply with the contour style of each radiation oncologist separately.

Purpose/Objective(s) Propagation of contours from high-quality magnetic resonance (MR) images to treatment planning ultrasound (US) images with severe needle artifacts is a challenging task, which can greatly aid the organ or intraprostatic lesion contouring in high dose rate (HDR) prostate brachytherapy. In this study, a deep learning approach was developed to automatize this registration procedure for HDR brachytherapy practice. Materials/Methods With the approval from the institutional review board, the MR and US images with manual prostate contours were collected from our clinical database and processed to construct the deep learning dataset. A deep learning framework based on the weakly-supervised strategy was proposed for the registration of prostate MR images and US images after the needle insertion, which consisted of two segmentation networks and a deformable registration network. Specifically, two 3D V-Net was trained for the prostate segmentation on the MR and US images separately, to generate the weak supervision labels automatically for the registration network training. Besides the image pair, the corresponding prostate probability maps from the segmentation were further fed to the registration network to improve the registration accuracy, and the deformation matrix was the output. A random augmentation method was designed to alleviate the map overfitting problem during the network training. The overlap between the deformed and fixed prostate contours was analyzed to evaluate the registration accuracy. Results Clinical data from 121, 104 and 63 patient cases were collected and utilized for the MR and US image segmentation networks, and the registration network learning, respectively. The mean DSC, center-of-mass (COM) distance, Hausdorff distance (HD) and averaged symmetric surface distance (ASSD) results for the registration of prostate contours were 0.87 ± 0.05, 1.70 ± 0.89 mm, 7.21 ± 2.07 mm, 1.61 ± 0.64 mm, respectively. By providing the prostate probability map from the segmentation to the registration network, as well as applying the random map augmentation method, the evaluation results of the four metrics were all improved, such as an increase of DSC from 0.83 ± 0.08 to 0.86 ± 0.06 and from 0.86 ± 0.06 to 0.87 ± 0.05, respectively. Conclusion This study first worked on the deep learning-based registration about the treatment planning US image for HDR prostate brachytherapy. A novel segmentation-based registration framework was proposed for this multi-modality registration problem, which not only largely saved the labor work on the data preparation, but also improved the registration accuracy. The evaluation results showed the potential of this approach in HDR brachytherapy to automatize current clinical procedures.

Purpose/Objective(s) Surveillance after definitive radiotherapy for oropharyngeal squamous cell carcinoma (OPSCC) is critical to confirm clinical complete response, as a subset of patients develop locoregional recurrences (LRR). The Hopkins Criteria are a standardized qualitative system for assessing response using post-treatment PET/CT and are validated for overall (OS) and progression-free survival (PFS). We hypothesized that the Hopkins Criteria score is independently prognostic for LRR. Materials/Methods We conducted an institutional retrospective study of adult patients with newly-diagnosed node-positive non-metastatic OPSCC treated with definitive (chemo)radiotherapy. Patients were eligible if they completed a pre- and post-radiotherapy PET/CT within 24 weeks after treatment. The primary outcome was the cumulative incidence of locoregional recurrence/persistence. A single board-certified nuclear medicine physician assigned Hopkins scores, which were considered either positive (scores 4-5) or negative (scores 1-3). Results Between 2004-2018, 259 patients were included in this analysis, while an additional 221 were excluded, most commonly due to lack of post-treatment PET/CT [37%] or N0 disease [22%]. Most patients were men (90%), 47% had never smoked, 86% had p16+/HPV+ tumors, and 39% had T3-4 tumors. Nearly all (98%) received concurrent chemotherapy, while 11% received induction chemotherapy. Median clinical follow-up was 40 months. Following radiotherapy, the first post-treatment PET/CT was positive (score 4-5) at the primary site in 32 patients (12%), cervical lymph nodes in 26 patients (10%), and either the primary site or neck in 50 patients (19%). This PET/CT was performed at a median of 3.3 months [range, 1.6-6.0] after radiotherapy. Thirty-four (13%) LRRs were observed. The sensitivity, specificity, negative predictive value, and overall accuracy of the Hopkins score for LRR were 68%, 88%, 95%, and 85%. The 36-month cumulative incidence of LRR was greater among patients with Hopkins scores 4-5 (45% vs. 5%, multivariable-adjusted HR 14.16, P Conclusion There is a lack of consensus reporting standards for response assessment after definitive radiotherapy for OPSCC. The Hopkins Criteria are an easily-implemented standard that appear to offer high accuracy for recurrence and death. These data support an initial PET/CT at least three months after radiotherapy to limit unnecessary diagnostic procedures.

PURPOSE/OBJECTIVE(S) To develop an unsupervised deep learning model with auto-mapped control volume (CV) from daily patient positioning CT (dCT) to planning computed tomography (pCT) for highly accurate patient positioning. MATERIALS/METHODS An unsupervised learning framework is proposed to automatically generate the couch shifts (translations and rotations) for mapping CVs from dCT to pCT. Inputs to the network are the dCT, the pCT, and the CVs' locations within the pCT. The outputs are the transformational parameters of the dCT for head-and-neck cancer (HNC) patient positioning. We train the network to maximize image similarity between the CV in the pCT and dCT using normalized cross-correlation. We used a total of 158 HNC patients with 554 CT scans for network evaluation. Each patient underwent several CT scans at different time points. For the test cases, couch shifts are obtained by averaging translational and rotational parameters derived with different CVs. These means are then compared to ground-truth reference shifts obtained by the alignment of bony landmarks identified by an experienced radiation oncologist. RESULTS Systematic/random positioning errors between the model prediction and the reference are smaller than 0.47/1.13 mm and 0.17/0.29° in translations and rotations, respectively. Pearson's correlation coefficient between model predictions and reference values exceeded 0.98. In comparison to standard registrations, the proposed method increased the proportion of cases registered within clinically accepted tolerance from 66.67% to 90.91%. CONCLUSION A novel unsupervised learning technique was established to map CVs from pCT to dCT for HNC patient positioning. Our results show that fast and highly accurate HNC patient positioning is achievable by leveraging state-of-the-art deep learning strategies.

Purpose/Objective(s) To evaluate Biology-guided Radiotherapy (BgRT) plans generated using PET data acquired on the first installation of the Reflexion™ X1 system. BgRT treatment planning optimizes for fluence indirectly by calculating firing filters that best translate the X1 acquired PET image to the fluences that deliver the prescribed radiation dose. During a treatment session, the firing filters are applied to limited time sampled (LTS) PET images to calculate delivery fluences. Materials/Methods An FDG fillable phantom insert that consists of a 22 mm diameter spherical target (PTV1, ball) and a C-shape object (PTV2 with 26mm axial length, 255° partial annulus with major and minor radii of 27 mm and 12 mm) was imaged using a CT Simulator to create planning contours. The water filled insert surrounding these objects was filled with FDG to simulate a warm background. Both targets were filled with FDG, simulating a target to background ratio of 8:1. Two different BgRT treatment plans were generated for PTV1 simulating homogenous uptake, and PTV2 with an inhomogeneous FDG uptake due to necrosis (a cylinder encompassing the C shape PET avid object). A structure that is 12.5 mm away from these targets was used to simulate OAR. Two PET/CT imaging acquisitions were performed on the RefleXion X1 masking the signal outside of each target plus 10 mm margin. Two different BgRT plans were generated prescribing 50 Gy delivered in 5 fractions to each target, separately. The quality of each treatment plan was evaluated based on PTV coverage, Conformity Index at 50% and 80% isodose lines (CI50, CI80) and OAR sparing. Both BgRT plans then delivered to the independent quality assurance tool for patient specific QA verification where the 3%/3 mm gamma criteria was used. Results BgRT plans for both homogenous and heterogenous FDG uptake targets were created successfully. Prescription dose coverages were 94.2% and 96.3% for PTV1 and PTV2, respectively. The maximum OAR doses were 30.9 and 33.0 Gy and average OAR doses were 14.4 and 20.1 Gy for PTV1 and PTV2 plans, respectively. CI50 were 7.7 and 6.8 where CI80 were 2.5 and 2.6 for PTV1 and PTV2 plans. The patient specific QA passed with 100 % for both plans. Conclusion This is the first report of the BgRT plan creation and QA delivery using a clinical installation of the Reflexion X1 system, opening the potential towards BgRT dose delivery using the real time FDG uptake measurements to biologically guide active radiation beam delivery. The X1 system was able to optimize and deliver the BgRT plans for both homogenous and heterogenous FDG uptake scenarios. BgRT is a new paradigm of dose optimization, where a new set of optimization and evaluation approaches will be needed.

8 Background: Patients with metastatic cancer benefit from advance care planning (ACP) conversations. Despite initiatives which train providers to have ACP conversations using the serious illness care program (SICP) conversation guide, few patients have a documented prognosis discussion due to busy clinic schedules and difficulty in deciding the right times to have such conversations. We designed an intervention to improve ACP by incorporating a validated computer model to identify patients at high risk for mortality in combination with lay care coaches. We investigated whether this would improve end of life quality measures. Methods: Four Stanford clinics were included in this pilot; all received SICP training. Two clinics (thoracic and genitourinary) underwent the intervention (computer model + care coach), and two clinics (sarcoma and cutaneous) served as the control. For providers in the intervention, an email was sent every Sunday listing the metastatic cancer patients who would be seen in clinic the following week and a predicted prognosis generated by the model. A lay care coach contacted patients with a predicted survival ≤2 years to have an ACP conversation with them. After, the care coach notified the provider to suggest discussion regarding prognosis with the patient. Criteria for a patient visit to be included in the analysis were: age ≥18, established patient, has sufficient EMR data for computer model, and no prior prognosis documentation. The primary outcome was documentation of prognosis in the ACP form by the end of the week following the clinic visit. Results: 5330 visits in 1298 unique patients met the inclusion criteria. Median age was 67 (range 19-97); 790 male, 508 female. 1970 visits were with patients with ≤2 year predicted survival. Prognosis discussion was documented by providers in the ACP form for 8.1% of intervention visits compared to 0.07% of control visits (p=0.001 in mixed effects model). Of the 1298 unique patients, 84 were deceased by December 2020. 41.7% died in the hospital. 59.5% were enrolled in hospice prior to death, and 19.0% were hospitalized in the ICU ≤14 days prior to death. Of deceased patients with ACP form prognosis documentation, 5.0% had ≥2 hospitalizations in the 30 days before death compared to 23.4% of deceased patients with no prognosis documented (p=0.10). For ≥ 2 ER visits in the 30 days before death, the proportions were 5.0% and 20.3% (p=0.17). Conclusions: This pilot study supports that our intervention is associated with higher rates of prognosis discussions and documentation. There was a trend towards better quality of end of life care as noted by higher rates of hospice enrollment and less intensive care at end of life. These results merit further investigation as a means to improve goal-concordant care and ensure appropriate care for cancer patients at the end of life.

Background and purpose Pre- and mid-radiotherapy FDG-PET metrics have been proposed as biomarkers of recurrence and survival in patients treated for stage III non-small cell lung cancer. We evaluated these metrics in patients treated with definitive radiation therapy (RT). We also evaluated outcomes after progression on mid-radiotherapy PET/CT. Material and methods Seventy-seven patients treated with RT with or without chemotherapy were included in this retrospective study. Primary tumor and involved nodes were delineated. PET metrics included metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUVmax. For mid-radiotherapy PET, both absolute value of these metrics and percentage decrease were analyzed. The influence of PET metrics on time to death, local recurrence, and regional/distant recurrence was assessed using Cox regression. Results 91% of patients had concurrent chemotherapy. Median follow-up was 14 months. None of the PET metrics were associated with overall survival. Several were positively associated with local recurrence: pre-radiotherapy MTV, and mid-radiotherapy MTV and TLG (p = 0.03–0.05). Ratio of mid- to pre-treatment SUVmax was associated with regional/distant recurrence (p = 0.02). 5/77 mid-radiotherapy scans showed early out-of-field progression. All of these patients died. Conclusions Several PET metrics were associated with risk of recurrence. Progression on mid-radiotherapy PET/CT was a poor prognostic factor.

Purpose Stereotactic ablative radiation therapy (SABR) is an established treatment for selected lung tumors. Sinoatrial node (SAN) toxicity after thoracic SABR has not been reported in the literature. We sought to understand the risk of SAN toxicity owing to incidental dose to the SAN from SABR. Methods and materials We conducted a retrospective review of patients with early-stage lung cancer or limited pulmonary metastases who underwent thoracic SABR to a right-sided central lung tumor (within 2 cm of the mainstem bronchus or other mediastinal structures) between January 2008 and December 2014, analyzed a subset whose treatment imparted dose to the SAN exceeding 10% of the prescription dose, and examined patient and treatment dosimetric characteristics. Mean follow-up interval was 28 months. Time to toxicity was defined from start of SABR. Results Of 47 patients with central tumors in the right lung treated with SABR reviewed, 13 met our study criteria. A contouring atlas of regional cardiac anatomy was created. One patient treated with SABR for non-small cell lung cancer at the right hilum developed symptomatic sick sinus syndrome, requiring pacemaker placement 6 months after treatment. Her acute presentation and short interval between SABR and onset of symptoms suggest that SAN toxicity was likely due to radiation-induced injury. Both her age and mean dose to her SAN were the third highest in our cohort. She remained free from cancer progression at 24 months' follow-up. Twelve additional patients who received significant dose to the SAN from SABR did not develop toxicity. Conclusion While uncommon, SAN toxicity from SABR to right-sided central thoracic tumors should be recognized and followed in future studies.

To evaluate the prognostic value and molecular basis of a CT-derived pleural contact index (PCI) in early stage non-small cell lung cancer (NSCLC). We retrospectively analysed seven NSCLC cohorts. A quantitative PCI was defined on CT as the length of tumour-pleura interface normalised by tumour diameter. We evaluated the prognostic value of PCI in a discovery cohort (n = 117) and tested in an external cohort (n = 88) of stage I NSCLC. Additionally, we identified the molecular correlates and built a gene expression-based surrogate of PCI using another cohort of 89 patients. To further evaluate the prognostic relevance, we used four datasets totalling 775 stage I patients with publically available gene expression data and linked survival information. At a cutoff of 0.8, PCI stratified patients for overall survival in both imaging cohorts (log-rank p = 0.0076, 0.0304). Extracellular matrix (ECM) remodelling was enriched among genes associated with PCI (p = 0.0003). The genomic surrogate of PCI remained an independent predictor of overall survival in the gene expression cohorts (hazard ratio: 1.46, p = 0.0007) adjusting for age, gender, and tumour stage. CT-derived pleural contact index is associated with ECM remodelling and may serve as a noninvasive prognostic marker in early stage NSCLC. • A quantitative pleural contact index (PCI) predicts survival in early stage NSCLC. • PCI is associated with extracellular matrix organisation and collagen catabolic process. • A multi-gene surrogate of PCI is an independent predictor of survival. • PCI can be used to noninvasively identify patients with poor prognosis.

We built a natural language processing (NLP) language model that can be used to extract cancer treatment information using structured and unstructured electronic medical records (EMR). Our work appears to be the first that combines EMR and NLP for treatment identification.