Defining Minimum Treatment Parameters of Ablative Radiation Therapy in Patients With Hepatocellular Carcinoma: An Expert Consensus.

PURPOSE: External beam radiation therapy (EBRT) is a highly effective treatment in select patients with hepatocellular carcinoma (HCC). However, the Barcelona Clinic Liver Cancer system does not recommend the use of EBRT in HCC due to a lack of sufficient evidence and intends to perform an individual patient level meta-analysis of ablative EBRT in this population. However, there are many types of EBRT described in the literature with no formal definition of what constitutes "ablative." Thus, we convened a group of international experts to provide consensus on the parameters that define ablative EBRT in HCC. METHODS AND MATERIALS: Fundamental parameters related to dose, fractionation, radiobiology, target identification, and delivery technique were identified by a steering committee to generate 7 Key Criteria (KC) that would define ablative EBRT for HCC. Using a modified Delphi (mDelphi) method, experts in the use of EBRT in the treatment of HCC were surveyed. Respondents were given 30 days to respond in round 1 of the mDelphi and 14 days to respond in round 2. A threshold of ≥70% was used to define consensus for answers to each KC. RESULTS: Of 40 invitations extended, 35 (88%) returned responses. In the first round, 3 of 7 KC reached consensus. In the second round, 100% returned responses and consensus was reached in 3 of the remaining 4 KC. The distribution of answers for one KC, which queried the a/b ratio of HCC, was such that consensus was not achieved. Based on this analysis, ablative EBRT for HCC was defined as a BED10 ≥80 Gy with daily imaging and multiphasic contrast used for target delineation. Treatment breaks (eg, for adaptive EBRT) are allowed, but the total treatment time should be ≤6 weeks. Equivalent dose when treating with protons should use a conversion factor of 1.1, but there is no single conversion factor for carbon ions. CONCLUSIONS: Using a mDelphi method assessing expert opinion, we provide the first consensus definition of ablative EBRT for HCC. Empirical data are required to define the a/b of HCC.

Patterns of Recurrence After Poor Response to Neoadjuvant Chemotherapy in Gastric Cancer and the Role for Adjuvant Radiation.

BACKGROUND: Improved treatment strategies are needed for patients with locally advanced gastric cancer with poor response to neoadjuvant chemotherapy. We aimed to describe patterns of failure for patients with no or partial response (NR, PR) to preoperative chemotherapy. PATIENTS AND METHODS: We analyzed patients with locally advanced gastric cancer treated from 2008 to 2022 with preoperative chemotherapy followed by surgery with D2 resection. We excluded patients who received radiation. Cumulative incidence of locoregional failure (LRF) and distant metastases (DM) were calculated. For patients with recurrent abdominal disease, hypothetical radiation clinical treatment volumes (CTV) were contoured on postoperative scans and compared with patterns of recurrence. RESULTS: A total of 60 patients were identified. The most used preoperative chemotherapy was FLOT (38.6%), followed by FOLFOX (30%) and ECF/ECX/EOX (23.3%). Four (6.7%), 40 (66.7%), and 9 patients (15%) had a complete pathologic response (CR), PR, and NR to neoadjuvant therapy, respectively. Among patients without a CR, 3-year overall and progression-free survival rates were 62.3% (95% CI 48-76.6%) and 51.3% (95% CI 36.9-65.7%), respectively. Three-year cumulative incidence of LRF and DM were 8.4% (95% CI 0.4-16.4%) and 41.0% (95% CI 26.3-55.4%), respectively. Absolute rates of patients having the first site of recurrence encompassed by a postoperative radiation CTV was 2.0% for patients without a CR and 0% for patients with NR. CONCLUSIONS: Patients with locally advanced gastric cancer with less than a CR to chemotherapy have poor outcomes due to high rates of DM. Adjuvant locoregional therapy such as radiation is unlikely to affect survival.

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.

Does Vascular Collapse Occur After Treatment of Hepatocellular Cancer With Stereotactic Body Radiation Therapy?

There is debate about why stereotactic body radiation therapy (SBRT) produces superior control of hepatocellular cancer (HCC) compared to fractionated treatment. Both preclinical and clinical evidence has been presented to support a "classic" biological explanation: the greater BED of SBRT produces more DNA damage and tumor cell kill. More recently, preclinical evidence has supported the concept of a "new biology", particularly radiation-induced vascular collapse, which increases hypoxia and free radical activation. This is hypothesized to cause much greater tumor cell death than was produced by the initial radiation-induced DNA damage to the tumor. We decided to investigate if vascular collapse occurs after standard SBRT for patients with HCC. Eight patients with 10 lesions underwent dynamic contrast enhanced MRI at the time of simulation and either 48 or 96 hours after the first fraction. Only three of 10 tumors showed a decrease in blood flow. These findings suggest that vascular collapse does not typically occur after SBRT for HCC.

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.

Is Apparent Diffusion Coefficient Established as an Imaging Biomarker for Stereotactic Body Radiation Therapy Assessment in Hepatocellular Carcinoma?

ABSTRACT: In this article, as part of this special issue on biomarkers of early response, we review currently available reports regarding magnetic resonance imaging apparent diffusion coefficient (ADC) changes in hepatocellular carcinoma (HCC) in response to stereotactic body radiation therapy. We compare diffusion image acquisition, ADC analysis, methods for HCC response assessment, and statistical methods for prediction of local tumor progression by ADC metrics. We discuss the pros and cons of these studies. Following detailed analyses of existing investigations, we cannot conclude that ADC is established as an imaging biomarker for stereotactic body radiation therapy assessment in HCC.

Adaptive Region-Specific Loss for Improved Medical Image Segmentation.

Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.

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.

Transparency in Modeling through Careful Application of OECD's QSAR/QSPR Principles via a Curated Water Solubility Data Set.

The need for careful assembly, training, and validation of quantitative structure-activity/property models (QSAR/QSPR) is more significant than ever as data sets become larger and sophisticated machine learning tools become increasingly ubiquitous and accessible to the scientific community. Regulatory agencies such as the United States Environmental Protection Agency must carefully scrutinize each aspect of a resulting QSAR/QSPR model to determine its potential use in environmental exposure and hazard assessment. Herein, we revisit the goals of the Organisation for Economic Cooperation and Development (OECD) in our application and discuss the validation principles for structure-activity models. We apply these principles to a model for predicting water solubility of organic compounds derived using random forest regression, a common machine learning approach in the QSA/PR literature. Using public sources, we carefully assembled and curated a data set consisting of 10,200 unique chemical structures with associated water solubility measurements. This data set was then used as a focal narrative to methodically consider the OECD's QSA/PR principles and how they can be applied to random forests. Despite some expert, mechanistically informed supervision of descriptor selection to enhance model interpretability, we achieved a model of water solubility with comparable performance to previously published models (5-fold cross validated performance 0.81 R2 and 0.98 RMSE). We hope this work will catalyze a necessary conversation around the importance of cautiously modernizing and explicitly leveraging OECD principles while pursuing state-of-the-art machine learning approaches to derive QSA/PR models suitable for regulatory consideration.

Outcomes and Imaging Analysis in Hepatocellular Carcinoma Treated With Stereotactic Body Radiation Therapy.

PURPOSE: Although arterial phase enhancement is commonly used to evaluate treatment response for hepatocellular carcinoma, it may not accurately describe response for lesions treated with stereotactic body radiation therapy (SBRT). We aimed to describe the post-SBRT imaging findings to better inform the optimal timing of salvage therapy after SBRT. METHODS AND MATERIALS: We retrospectively reviewed patients with hepatocellular carcinoma treated with SBRT from 2006 to 2021 at a single institution with available imaging showing lesions with characteristic arterial enhancement and portal venous washout. Patients were then stratified into 3 groups based on treatment: (1) concurrent SBRT and transarterial chemoembolization, (2) SBRT only, and (3) SBRT followed by early salvage therapy due to persistent enhancement. Overall survival was analyzed with the Kaplan-Meier method, and cumulative incidences were calculated with competing risk analysis. RESULTS: We included 82 lesions in 73 patients. The median follow-up time was 22.3 months (range, 2.2-88.1 months). The median time to overall survival was 43.7 months (95% confidence interval, 28.1-57.6 months) and median progression-free survival was 10.5 months (95% confidence interval, 7.2-14.0 months). There were 10 (12.2%) lesions that experienced local progression and there was no difference in rates of local progression between the 3 groups (P = .32). In the SBRT-only group, the median time to resolution of arterial enhancement and washout was 5.3 months (range, 1.6-23.7 months). At 3, 6, 9, and 12 months, 82%, 41%, 13%, and 8% of lesions, respectively, continued to show arterial hyperenhancement. CONCLUSIONS: Tumors treated with SBRT may continue to exhibit persistence of arterial hyperenhancement. Without an increase in size of enhancement, continued surveillance may be appropriate for these patients.

Validation of a Resectability Scoring System for Prediction of Pancreatic Adenocarcinoma Surgical Outcomes.

BACKGROUND: The most used pancreatic cancer (PC) resectability criteria are descriptive in nature or based solely on dichotomous degree of involvement (< 180° or > 180°) of vessels, which allows for a high degree of subjectivity and inconsistency. METHODS: Radiographic measurements of the circumferential degree and length of tumor contact with major peripancreatic vessels were retrospectively obtained from pre-treatment multi-detector computed tomography (MDCT) images from PC patients treated between 2001 and 2015 at two large academic institutions. Arterial and venous scores were calculated for each patient, then tested for a correlation with tumor resection and R0 resection. RESULTS: The analysis included 466 patients. Arterial and venous scores were highly predictive of resection and R0 resection in both the training (n = 294) and validation (n = 172) cohorts. A recursive partitioning tree based on arterial and venous score cutoffs developed with the training cohort was able to stratify patients of the validation cohort into discrete groups with distinct resectability probabilities. A refined recursive partitioning tree composed of three resectability groups was generated, with probabilities of resection and R0 resection of respectively 94 and 73% for group A, 61 and 35% for group B, and 4 and 2% for group C. This resectability scoring system (RSS) was highly prognostic, predicting median overall survival times of 27, 18.9, and 13.5 months respectively for patients in RSS groups A, B, and C (p < 0.001). CONCLUSIONS: The proposed RSS was highly predictive of resection, R0 resection, and prognosis for patients with PC when tested against an external dataset.

Neoadjuvant Therapy in the Post-German Rectal Trial Era: Making Sense in the Absence of Consensus.

Trimodality therapy per the German Rectal Trial has led to excellent locoregional outcomes for locally advanced rectal cancer. Recent efforts have shifted toward improving distant control and health-related quality of life in this disease. To this end, total neoadjuvant therapy has become an increasingly used approach in which most, if not all, chemotherapy is delivered before surgery to improve compliance and to address micrometastases early. To avoid surgical morbidity, a "watch-and-wait" approach, in which total mesorectal excision is deferred, has also been studied for patients who achieve a clinical complete response after chemoradiation. These 2 concurrent treatment trends have raised many points of uncertainty in what used to be a relatively straightforward neoadjuvant treatment paradigm. We discuss here our approach to neoadjuvant therapy for locally advanced rectal cancer, based on the data we currently have and through shared decision-making with patients to help them select the treatment that best aligns with their preferences and values.

Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity.

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intratumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in > 90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intratumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC. SIGNIFICANCE: A high-throughput pipeline using multiplex immunofluorescence in pancreatic cancer reveals striking expression subtype intratumoral heterogeneity with implications for therapy selection and identifies co-expressor cells that may serve as intermediates during subtype switching.

Neoadjuvant Chemotherapy Is Associated with Altered Immune Cell Infiltration and an Anti-Tumorigenic Microenvironment in Resected Pancreatic Cancer.

PURPOSE: Neoadjuvant chemotherapy is increasingly administered to patients with resectable or borderline resectable pancreatic ductal adenocarcinoma (PDAC), yet its impact on the tumor immune microenvironment is incompletely understood. DESIGN: We employed quantitative, spatially resolved multiplex immunofluorescence and digital image analysis to identify T-cell subpopulations, macrophage polarization states, and myeloid cell subpopulations in a multi-institution cohort of up-front resected primary tumors (n = 299) and in a comparative set of resected tumors after FOLFIRINOX-based neoadjuvant therapy (n = 36) or up-front surgery (n = 30). Multivariable-adjusted Cox proportional hazards models were used to evaluate associations between the immune microenvironment and patient outcomes. RESULTS: In the multi-institutional resection cohort, immune cells exhibited substantial heterogeneity across patient tumors and were located predominantly in stromal regions. Unsupervised clustering using immune cell densities identified four main patterns of immune cell infiltration. One pattern, seen in 20% of tumors and characterized by abundant T cells (T cell-rich) and a paucity of immunosuppressive granulocytes and macrophages, was associated with improved patient survival. Neoadjuvant chemotherapy was associated with a higher CD8:CD4 ratio, greater M1:M2-polarized macrophage ratio, and reduced CD15+ARG1+ immunosuppressive granulocyte density. Within neoadjuvant-treated tumors, 72% showed a T cell-rich pattern with low immunosuppressive granulocytes and macrophages. M1-polarized macrophages were located closer to tumor cells after neoadjuvant chemotherapy, and colocalization of M1-polarized macrophages and tumor cells was associated with greater tumor pathologic response and improved patient survival. CONCLUSIONS: Neoadjuvant chemotherapy with FOLFIRINOX shifts the PDAC immune microenvironment toward an anti-tumorigenic state associated with improved patient survival.

The Combination of Radiotherapy and Complement C3a Inhibition Potentiates Natural Killer cell Functions Against Pancreatic Cancer.

Pancreatic cancer is one of the deadliest cancers, against which current immunotherapy strategies are not effective. Herein, we analyzed the immune cell composition of the tumor microenvironment of pancreatic cancer samples in The Cancer Genome Atlas and found that the presence of intratumoral NK cells correlates with survival. Subsequent analysis also indicated that NK cell exclusion from the microenvironment is found in a high percentage of clinical pancreatic cancers and in preclinical models of pancreatic cancer. Mechanistically, NK cell exclusion is regulated in part by complement C3a and its receptor signaling. Inhibition of the C3a receptor enhances NK cell infiltration in syngeneic mouse models of pancreatic cancer resulting in tumor growth delay. However, tumor growth inhibition mediated by NK cells is not sufficient alone for complete tumor regression, but is potentiated when combined with radiation therapy. Our findings indicate that although C3a inhibition is a promising approach to enhance NK cell-based immunotherapy against pancreatic cancer, its combination with radiation therapy hold greater therapeutic benefit.