Chest wall pain after single-fraction thoracic stereotactic ablative Radiotherapy: Dosimetric analysis from the iSABR trial.

BACKGROUND AND PURPOSE: Concerns over chest wall toxicity has led to debates on treating tumors adjacent to the chest wall with single-fraction stereotactic ablative radiotherapy (SABR). We performed a secondary analysis of patients treated on the prospective iSABR trial to determine the incidence and grade of chest wall pain and modeled dose-response to guide radiation planning and estimate risk. MATERIALS AND METHODS: This analysis included 99 tumors in 92 patients that were treated with 25 Gy in one fraction on the iSABR trial which individualized dose by tumor size and location. Toxicity events were prospectively collected and graded based on the CTCAE version 4. Dose-response modeling was performed using a logistic model with maximum likelihood method utilized for parameter fitting. RESULTS: There were 22 grade 1 or higher chest wall pain events, including five grade 2 events and zero grade 3 or higher events. The volume receiving at least 11 Gy (V11Gy) and the minimum dose to the hottest 2 cc (D2cc) were most highly correlated with toxicity. When dichotomized by an estimated incidence of ≥ 20 % toxicity, the D2cc > 17 Gy (36.6 % vs. 3.7 %, p < 0.01) and V11Gy > 28 cc (40.0 % vs. 8.1 %, p < 0.01) constraints were predictive of chest wall pain, including among a subset of patients with tumors abutting or adjacent to the chest wall. CONCLUSION: For small, peripheral tumors, single-fraction SABR is associated with modest rates of low-grade chest wall pain. Proximity to the chest wall may not contraindicate single fractionation when using highly conformal, image-guided techniques with sharp dose gradients.

Surrogate endpoints in clinical trials of p16-positive squamous cell carcinoma of the oropharynx: an individual patient data meta-analysis.

BACKGROUND: The increased incidence of human papillomavirus (HPV)-related cancers has motivated efforts to optimise treatment for these patients with excellent prognosis. Validation of surrogates for overall survival could expedite the investigation of new therapies. We sought to evaluate candidate intermediate clinical endpoints in trials assessing definitive treatment of p16-positive oropharyngeal cancer with chemotherapy or radiotherapy. METHODS: We did a retrospective review of five multicentre, randomised trials (NRG/RTOG 9003, 0129, 0234, 0522, and 1016) that tested radiotherapy with or without chemotherapy in patients (aged ≥18 years) with p16-positive localised head or neck squamous-cell carcinomas. Eight intermediate clinical endpoints were considered as potential surrogates for overall survival: freedom from local progression, freedom from regional progression, freedom from distant metastasis, freedom from locoregional progression, freedom from any progression, locoregional progression-free survival, progression-free survival, and distant metastasis-free survival. We used a two-stage meta-analytical framework, which requires high correlation between the intermediate clinical endpoint and overall survival at the patient level (condition 1), and high correlation between the treatment effect on the intermediate clinical endpoint and the treatment effect on overall survival (condition 2). For both, an r2 greater than 0·7 was used as criteria for clinically relevant surrogacy. FINDINGS: We analysed 1373 patients with oropharyngeal cancer from May 9, 2020, to Nov 22, 2023. 1231 (90%) of patients were men, 142 (10%) were women, and 1207 (88%) were White, with a median age of 57 years (IQR 51-62). Median follow-up was 4·2 years (3·1-5·1). For the first condition, correlating the intermediate clinical endpoints with overall survival at the individual and trial level, the three composite endpoints of locoregional progression-free survival (Kendall's τ 0·91 and r2 0·72), distant metastasis-free survival (Kendall's τ 0·93 and r2 0·83), and progression-free survival (Kendall's τ 0·88 and r2 0·70) were highly correlated with overall survival at the patient level and at the trial-group level. For the second condition, correlating treatment effects of the intermediate clinical endpoints and overall survival, the composite endpoints of locoregional progression-free survival (r2 0·88), distant metastasis-free survival (r2 0·96), and progression-free survival (r2 0·92) remained strong surrogates. Treatment effects on the remaining intermediate clinical endpoints were less strongly correlated with overall survival. INTERPRETATION: We identified locoregional progression-free survival, distant metastasis-free survival, and progression-free survival as surrogates for overall survival in p16-positive oropharyngeal cancers treated with chemotherapy or radiotherapy, which could serve as clinical trial endpoints. FUNDING: NRG Oncology Operations, NRG Oncology SDMC, the National Cancer Institute, Eli Lilly, Aventis, and the University of Michigan.

Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center.

Background: Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs' Serious Illness Care Program (SICP) at Stanford Health Care. Objective: Improve quantity, timing, and quality of serious illness conversations. Methods: Initial implementation followed Ariadne Labs' SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Results: Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification. Conclusion: Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.

BIOGUIDE-X: A First-in-Human Study of the Performance of Positron Emission Tomography-Guided Radiation Therapy.

PURPOSE: Positron emission tomography (PET)-guided radiation therapy is a novel tracked dose delivery modality that uses real-time PET to guide radiation therapy beamlets. The BIOGUIDE-X study was performed with sequential cohorts of participants to (1) identify the fluorodeoxyglucose (FDG) dose for PET-guided therapy and (2) confirm that the emulated dose distribution was consistent with a physician-approved radiation therapy plan. METHODS AND MATERIALS: This prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (2-5 cm) in the lung or bone. For cohort I, a modified 3 + 3 design was used to determine the FDG dose that would result in adequate signal for PET-guided therapy. For cohort II, PET imaging data were collected on the X1 system before the first and last fractions among patients undergoing conventional stereotactic body radiation therapy. PET-guided therapy dose distributions were modeled on the patient's computed tomography anatomy using the collected PET data at each fraction as input to an "emulated delivery" and compared with the physician-approved plan. RESULTS: Cohort I demonstrated adequate FDG activity in 6 of 6 evaluable participants (100.0%) with the first injected dose level of 15 mCi FDG. In cohort II, 4 patients with lung tumors and 5 with bone tumors were enrolled, and evaluable emulated delivery data points were collected for 17 treatment fractions. Sixteen of the 17 emulated deliveries resulted in dose distributions that were accurate with respect to the approved PET-guided therapy plan. The 17th data point was just below the 95% threshold for accuracy (dose-volume histogram score = 94.6%). All emulated fluences were physically deliverable. No toxicities were attributed to multiple FDG administrations. CONCLUSIONS: PET-guided therapy is a novel radiation therapy modality in which a radiolabeled tumor can act as its own fiducial for radiation therapy targeting. Emulated therapy dose distributions calculated from continuously acquired real-time PET data were accurate and machine-deliverable in tumors that were 2 to 5 cm in size with adequate FDG signal characteristics.

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

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

Individualized Stereotactic Ablative Radiotherapy for Lung Tumors: The iSABR Phase 2 Nonrandomized Controlled Trial.

Importance: Stereotactic ablative radiotherapy (SABR) is used for treating lung tumors but can cause toxic effects, including life-threatening damage to central structures. Retrospective data suggested that small tumors up to 10 cm3 in volume can be well controlled with a biologically effective dose less than 100 Gy. Objective: To assess whether individualizing lung SABR dose and fractionation by tumor size, location, and histological characteristics may be associated with local tumor control. Design, Setting, and Participants: This nonrandomized controlled trial (the iSABR trial, so named for individualized SABR) was a phase 2 multicenter trial enrolling participants from November 15, 2011, to December 5, 2018, at academic medical centers in the US and Japan. Data were analyzed from December 9, 2020, to May 10, 2023. Patients were enrolled in 3 groups according to cancer type: initial diagnosis of non-small cell lung cancer (NSCLC) with an American Joint Committee on Cancer 7th edition T1-3N0M0 tumor (group 1), a T1-3N0M0 new primary NSCLC with a history of prior NSCLC or multiple NSCLCs (group 2), or lung metastases from NSCLC or another solid tumor (group 3). Intervention: Up to 4 tumors were treated with once-daily SABR. The dose ranged from 25 Gy in 1 fraction for peripheral tumors with a volume of 0 to 10 cm3 to 60 Gy in 8 fractions for central tumors with a volume greater than 30 cm3. Main outcome: Per-group freedom from local recurrence (same-lobe recurrence) at 1 year, with censoring at time of distant recurrence, death, or loss to follow-up. Results: In total, 217 unique patients (median [IQR] age, 72 [64-80] years; 129 [59%] male; 150 [69%] current or former smokers) were enrolled (some multiple times). There were 240 treatment courses: 79 in group 1, 82 in group 2, and 79 in group 3. A total of 285 tumors (211 [74%] peripheral and 74 [26%] central) were treated. The most common dose was 25 Gy in 1 fraction (158 tumors). The median (range) follow-up period was 33 (2-109) months, and the median overall survival was 59 (95% CI, 49-82) months. Freedom from local recurrence at 1 year was 97% (90% CI, 91%-99%) for group 1, 94% (90% CI, 87%-97%) for group 2, and 96% (90% CI, 89%-98%) for group 3. Freedom from local recurrence at 5 years ranged from 83% to 93% in the 3 groups. The proportion of patients with grade 3 to 5 toxic effects was low, at 5% (including a single patient [1%] with grade 5 toxic effects). Conclusions and Relevance: The results of this nonrandomized controlled trial suggest that individualized SABR (iSABR) used to treat lung tumors may allow minimization of treatment dose and is associated with excellent local control. Individualized dosing should be considered for use in future trials. Trial Registration: ClinicalTrials.gov Identifier: NCT01463423.

Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer.

PURPOSE: For patients with cancer and their doctors, prognosis is important for choosing treatments and supportive care. Oncologists' life expectancy estimates are often inaccurate, and many patients are not aware of their general prognosis. Machine learning (ML) survival models could be useful in the clinic, but there are potential concerns involving accuracy, provider training, and patient involvement. We conducted a qualitative study to learn about patient and oncologist views on potentially using a ML model for patient care. METHODS: Patients with metastatic cancer (n = 15) and their family members (n = 5), radiation oncologists (n = 5), and medical oncologists (n = 5) were recruited from a single academic health system. Participants were shown an anonymized report from a validated ML survival model for another patient, which included a predicted survival curve and a list of variables influencing predicted survival. Semistructured interviews were conducted using a script. RESULTS: Every physician and patient who completed their interview said that they would want the option for the model to be used in their practice or care. Physicians stated that they would use an AI prognosis model for patient triage and increasing patient understanding, but had concerns about accuracy and explainability. Patients generally said that they would trust model results completely if presented by their physician but wanted to know if the model was being used in their care. Some reacted negatively to being shown a median survival prediction. CONCLUSION: Patients and physicians were supportive of use of the model in the clinic, but had various concerns, which should be addressed as predictive models are increasingly deployed in practice.

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

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. FUNDING: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Lessons and Opportunities for Biomarker-Driven Radiation Personalization in Head and Neck Cancer.

Head and neck cancer is notoriously challenging to treat in part because it constitutes an anatomically and biologically diverse group of cancers with heterogeneous prognoses. While treatment can be associated with significant late toxicities, recurrence is often difficult to salvage with poor survival rates and functional morbidity.1,2 Thus, achieving tumor control and cure at the initial diagnosis is the highest priority. Given the differing outcome expectations (even within a specific sub-site like oropharyngeal carcinoma), there has been growing interest in personalizing treatment: de-escalation in selected cancers to decrease the risk of late toxicity without compromising oncologic outcomes, and intensification for more aggressive cancers to improve oncologic outcomes without causing undue toxicity. This risk stratification is increasingly accomplished using biomarkers, which can represent molecular, clinicopathologic, and/or radiologic data. In this review, we will focus on biomarker-driven radiotherapy dose personalization with emphasis on oropharyngeal and nasopharyngeal carcinoma. This radiation personalization is largely performed on the population level by identifying patients with good prognosis via traditional clinicopathologic factors, although there are emerging studies supporting inter-tumor and intra-tumor level personalization via imaging and molecular biomarkers.

Unilateral Diaphragmatic Paralysis After Stereotactic Ablative Radiation Therapy to a Lung Tumor Abutting the Course of the Phrenic Nerve.

We present the case of a woman with metastatic adenoid cystic carcinoma who received stereotactic ablative radiation therapy with a total dose of 50 Gy in 4 fractions to 2 lung metastases and developed symptomatic left phrenic nerve injury 2 years after radiation. The maximum dose to the approximate location of the phrenic nerve was 57.7 Gy, which corresponds to a biologically effective dose for late effects (using α/ß ratio = 3) of 335.14 Gy. Here, we discuss the case, planning considerations by radiation oncologists and medical physicists, and the multidisciplinary medical management of this patient.

Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation Therapy.

PURPOSE: This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system. METHODS AND MATERIALS: For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning computed tomography (CT) scans and 5 to 26 sets of daily kVCT images were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric effects resulting from different auto-segmentation and registration methods were also investigated. RESULTS: The proposed patient-specific network achieved mean DSC results of 0.88 for 3 HaN organs at risk (OARs) of interest and 0.90 for 8 pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour. CONCLUSIONS: Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiation therapy.

Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.

PURPOSE: Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer Inc. is a deep learning algorithm with recent FDA clearance to assist in brain tumor contouring. We aimed to assess the performance of this tool by various demographic and clinical characteristics among patients with brain metastases treated with SRS. MATERIALS AND METHODS: We randomly selected 100 patients with brain metastases who underwent initial SRS on the CyberKnife from 2017 to 2020 at a single institution. Cases with resection cavities were excluded from the analysis. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. A brain metastasis was considered "detected" when the VBrain- "predicted" contours overlapped with the corresponding physician contours ("ground-truth" contours). We evaluated performance of VBrain against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%). Kruskal-Wallis tests were performed to assess the relationships between patient characteristics including sex, race, primary histology, age, and size and number of brain metastases, and performance metrics such as DSC, AVD, FP, and sensitivity. RESULTS: We analyzed 100 patients with 435 intact brain metastases treated with SRS. Our cohort consisted of patients with a median number of 2 brain metastases (range: 1 to 52), median age of 69 (range: 19 to 91), and 50% male and 50% female patients. The primary site breakdown was 56% lung, 10% melanoma, 9% breast, 8% gynecological, 5% renal, 4% gastrointestinal, 2% sarcoma, and 6% other, while the race breakdown was 60% White, 18% Asian, 3% Black/African American, 2% Native Hawaiian or other Pacific Islander, and 17% other/unknown/not reported. The median tumor size was 0.112 c.c. (range: 0.010-26.475 c.c.). We found mean lesion-wise DSC to be 0.723, mean lesion-wise AVD to be 7.34% of lesion size (0.704 mm), mean FP count to be 0.72 tumors per case, and lesion-wise sensitivity to be 89.30% for all lesions. Moreover, mean sensitivity was found to be 99.07%, 97.59%, and 96.23% for lesions with diameter equal to and greater than 10 mm, 7.5 mm, and 5 mm, respectively. No other significant differences in performance metrics were observed across demographic or clinical characteristic groups. CONCLUSION: In this study, a commercial deep learning algorithm showed promising results in segmenting brain metastases, with 96.23% sensitivity for metastases with diameters of 5 mm or higher. As the software is an assistive AI, future work of VBrain integration into the clinical workflow can provide further clinical and research insights.

Pulmonary Hemorrhage in Patients Treated With Thoracic Stereotactic Ablative Radiotherapy and Antiangiogenic Agents.

INTRODUCTION: Severe pulmonary hemorrhage can occur in patients treated with thoracic stereotactic ablative radiotherapy (SABR) and vascular endothelial growth factor inhibitors (VEGFis). There is limited understanding of which patients are at risk for toxicity with the combination of thoracic SABR and VEGFis or how the risk differs over either therapy alone. METHODS: We evaluated a prospectively maintained cohort of 690 patients with 818 pulmonary tumors treated with highly conformal SABR. Rates of any-grade and grade 3 plus (G3+) pulmonary hemorrhage were compared between patients treated with or without VEGFi therapy across tumor locations. Outcomes were compared between patients treated with SABR plus VEGFi and a propensity-matched cohort of those treated with VEGFi therapy alone. RESULTS: Treatment with VEGFi plus SABR was associated with higher rates of G3+ pulmonary hemorrhage compared with those treated with SABR alone for the overall cohort (3-y incidence: 7.9% versus 0.6%, p < 0.01) and those with central tumors (19.1% versus 3.3%, p = 0.04). When further subdivided, there were significantly higher toxicity rates with VEGFi for the ultracentral (9.0% versus 45.0%, p = 0.044), but not central nonabutting tumors (0.0% versus 1.3%, p = 0.69). There was an increased incidence of G3+ hemorrhage in patients treated with VEGFi plus SABR compared with VEGFi alone (9.6% versus 1.3%, p = 0.04). CONCLUSIONS: The combination of VEGFi and SABR was associated with an increased risk of high-grade pulmonary hemorrhage over either therapy alone. Low rates of toxicity were observed when excluding patients with SABR to ultracentral tumors and applying highly conformal SABR techniques.

Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer.

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.

Safety of Nivolumab Added to Chemoradiation Therapy Platforms for Intermediate and High-Risk Locoregionally Advanced Head and Neck Squamous Cell Carcinoma: RTOG Foundation 3504.

PURPOSE: 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. METHODS AND MATERIALS: 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. RESULTS: 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% had >10 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. CONCLUSIONS: 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).

Considerations in the reliability and fairness audits of predictive models for advance care planning.

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we conducted a reliability audit of two models based on model performance and calibration as well as a fairness audit based on summary statistics, subgroup performance and subgroup calibration. We assessed the Epic End-of-Life (EOL) Index model and an internally developed Stanford Hospital Medicine (HM) Advance Care Planning (ACP) model in 3 practice settings: Primary Care, Inpatient Oncology and Hospital Medicine, using clinicians' answers to the surprise question ("Would you be surprised if [patient X] passed away in [Y years]?") as a surrogate outcome. For performance, the models had positive predictive value (PPV) at or above 0.76 in all settings. In Hospital Medicine and Inpatient Oncology, the Stanford HM ACP model had higher sensitivity (0.69, 0.89 respectively) than the EOL model (0.20, 0.27), and better calibration (O/E 1.5, 1.7) than the EOL model (O/E 2.5, 3.0). The Epic EOL model flagged fewer patients (11%, 21% respectively) than the Stanford HM ACP model (38%, 75%). There were no differences in performance and calibration by sex. Both models had lower sensitivity in Hispanic/Latino male patients with Race listed as "Other." 10 clinicians were surveyed after a presentation summarizing the audit. 10/10 reported that summary statistics, overall performance, and subgroup performance would affect their decision to use the model to guide care; 9/10 said the same for overall and subgroup calibration. The most commonly identified barriers for routinely conducting such reliability and fairness audits were poor demographic data quality and lack of data access. This audit required 115 person-hours across 8-10 months. Our recommendations for performing reliability and fairness audits include verifying data validity, analyzing model performance on intersectional subgroups, and collecting clinician-patient linkages as necessary for label generation by clinicians. Those responsible for AI models should require such audits before model deployment and mediate between model auditors and impacted stakeholders.

Posttreatment FDG-PET/CT Hopkins criteria predict locoregional recurrence after definitive radiotherapy for oropharyngeal squamous cell carcinoma.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The Hopkins Criteria predict LRR with high accuracy for OPSCC response assessment.

Accuracy of Electronic Medical Record Follow-Up Data for Estimating the Survival Time of Patients With Cancer.

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.