Early regression index (ERI) on MR images as response predictor in esophageal cancer treated with neoadjuvant chemo-radiotherapy: Interim analysis of the prospective ESCAPE trial

PURPOSE

The early regression index (ERI) predicts treatment response in rectal cancer patients. Aim of current study was to prospectively assess tumor response to neoadjuvant chemo-radiotherapy (nCRT) of locally advanced esophageal cancer using ERI, based on MRI.

MATERIAL AND METHODS

From January 2020 to May 2023, 30 patients with esophageal cancer were enrolled in a prospective study (ESCAPE). PET-MRI was performed: i) before nCRT (tpre); ii) at mid-radiotherapy, tmid; iii) after nCRT, 2-6 weeks before surgery (tpost); nCRT delivered 41.4 Gy/23fr with concurrent carboplatin and paclitaxel. For patients that skipped surgery, complete clinical response (cCR) was assessed if patients showed no local relapse after 18 months; patients with pathological complete response (pCR) or with cCR were considered as complete responders (pCR + cCR). GTV volumes were delineated by two observers (Vpre, Vmid, Vpost) on T2w MRI: ERI and other volume regression parameters at tmid and tpost were tested as predictors of pCR + cCR.

RESULTS

Complete data of 25 patients were available at the time of the analysis: 3/25 with complete response at imaging refused surgery and 2/3 were cCR; in total, 10/25 patients showed pCR + cCR (pCR = 8/22). Both ERImid and ERIpost classified pCR + cCR patients, with ERImid showing better performance (AUC:0.78, p = 0.014): A two-variable logistic model combining ERImid and Vpre improved performances (AUC:0.93, p < 0.0001). Inter-observer variability in contouring GTV did not affect the results.

CONCLUSIONS

Despite the limited numbers, interim analysis of ESCAPE study suggests ERI as a potential predictor of complete response after nCRT for esophageal cancer. Further validation on larger populations is warranted.

Extended nodal radiotherapy for prostate cancer relapse guided with [11C]-choline PET/CT: ten-year results in patients enrolled in a prospective trial

AIMS

To report long-term outcomes of relapsed prostate cancer (PC) patients treated in a prospective single-arm study with extended-nodal radiotherapy (ENRT) and [11C]-choline positron emission tomography (PET)/computed tomography (CT)-guided simultaneous integrated boost (SIB) to positive lymph nodes (LNs).

METHODS

From 12/2009 to 04/2015, 60 PC patients with biochemical relapse and positive LNs only were treated in this study. ENRT at a median total dose (TD) = 51.8 Gy/28 fr and PET/CT-guided SIB to positive LNs at a median TD = 65.5 Gy was prescribed. Median PSA at relapse was 2.3 (interquartile range, IQR:1.3-4.0) ng/ml. Median number of positive LNs: 2 (range: 1-18). Androgen deprivation therapy (ADT) was prescribed for 48 patients for a median of 30.7 (IQR: 18.5-43.1) months.

RESULTS

Median follow-up from the end of salvage treatment was 121.8 (IQR: 116.1, 130.9) months; 3-, 5-, and 10-year BRFS were 45.0%, 36.0%, and 24.0%, respectively; DMFS: 67.9%, 57.2%, and 45.2%; CRFS: 62.9%, 53.9%, and 42.0%; and OS: 88.2%, 76.3%, and 47.9%, respectively. Castration resistance (p < 0.0001) and ≥ 6 positive LN (p = 0.0024) significantly influenced OS at multivariate analysis. Castration resistance (p < 0.0001 for both) influenced DMFS and CRFS in multivariate analysis.

CONCLUSIONS

In PC relapsed patients treated with ENRT and [11C]-choline-PET/CT-guided SIB for positive LNs, with 10-year follow-up, a median Kaplan-Meier estimate CRFS of 67 months and OS of 110 months were obtained. These highly favorable results should be confirmed in a prospective, randomized trial.

Dosimetry Predictors of Late Skin Reactions after Whole Breast Radiotherapy on a Large Mono-Institutional Cohort of Patients

PURPOSE/OBJECTIVE(S)

To develop an NTCP model predicting late skin toxicity using dosimetric parameters from the breast dermis to identify possible RT constraints on such a structure.

MATERIALS/METHODS

The skin structure was defined as the 5 mm inner isotropic expansion from the outer CT body contour. It was retrospectively segmented on a large mono-Institutional cohort of early-stage breast cancer patients enrolled between 2009 and 2017 (n = 1066). Patients were treated with tangential-field RT, delivering 40 Gy in 15 fractions without a RT boost. Toxicity was reported during FU using SOMA/LENT scoring. The study endpoint was moderate-severe late toxicity consisting of Fibrosis-Atrophy-Telangiectasia-Pain (FATP G2+) developed within 42 months after RT completion. Automatic delineation of skin and DVH extraction were accomplished by scripting using the MIM_assistant software. Also, the impact of changes in the dose calculation algorithms during enrolment time was quantified. A logistic model was created by combining multifactorial variables, considering both clinical factors and the absolute skin DVH (cc). Variance Inflation Factor (VIF) was performed to reduce the multicollinearity. Repeated 5-fold cross-validation with SMOTE approach to overcome the class unbalance was applied for model feature selection. The predictive model was then developed on the entire population due to the limited G2+ events.

RESULTS

The FATP G2+ rate was 3.8% with 40/1066 experiencing late toxicity. Among them, a 40% had already developed acute symptoms after RT completion showing a consequential effect. The multicollinearity analysis selected 27 clinical-treatment-dosimetric factors. After repeated (20 times) 5-fold cross-validation, the best-performing model included Post-Surgery Cosmetic alterations, Aromatase Inhibitors (as a protective factor), V20 Gy (50% of the prescribed dose - DVH plateau region) and V42 Gy (105% of the prescribed dose - DVH high-dose tail). Accuracy and f1-score were 0.76 and 0.58 in both training and test sets, providing good reliability for selected variables. AUC for the final model on the entire population was 0.76+/-0.04.

CONCLUSION

We quantified the association between fibrosis and skin DVH when delivering 40 Gy in 15fr. The model suggested an independent role of V20 and V42 Gy and a heavy risk modulation by surgical effects and aromatase inhibitors. This last factor could interfere with adipose tissue and water-content distribution within the breast. For this purpose, a CT-based densitometry characterization of toxicity patients is ongoing.

Voxel-wise analysis: A powerful tool to predict radio-induced toxicity and potentially perform personalised planning in radiotherapy

Dose - volume histograms have been historically used to study the relationship between the planned radiation dose and healthy tissue damage. However, this approach considers neither spatial information nor heterogenous radiosensitivity within organs at risk, depending on the tissue. Recently, voxel-wise analyses have emerged in the literature as powerful tools to fully exploit three-dimensional information from the planned dose distribution. They allow to identify anatomical subregions of one or several organs in which the irradiation dose is associated with a given toxicity. These methods rely on an accurate anatomical alignment, usually obtained by means of a non-rigid registration. Once the different anatomies are spatially normalised, correlations between the three-dimensional dose and a given toxicity can be explored voxel-wise. Parametric or non-parametric statistical tests can be performed on every voxel to identify the voxels in which the dose is significantly different between patients presenting or not toxicity. Several anatomical subregions associated with genitourinary, gastrointestinal, cardiac, pulmonary or haematological toxicity have already been identified in the literature for prostate, head and neck or thorax irradiation. Voxel-wise analysis appears therefore first particularly interesting to increase toxicity prediction capability by identifying specific subregions in the organs at risk whose irradiation is highly predictive of specific toxicity. The second interest is potentially to decrease the radio-induced toxicity by limiting the dose in the predictive subregions, while not decreasing the dose in the target volume. Limitations of the approach have been pointed out.

Dependence of the AUC of NTCP models on the observational dose-range highlights cautions in comparison of discriminative performance

BACKGROUND

Normal tissue complication probability (NTCP) models are probabilistic models that describe the risk of radio-induced toxicity in tissues or organs. In the field of radiotherapy, the area under the ROC curve (AUC) is widely used to estimate the performance in risk prediction of NTCP models.

METHODS

In this work, we derived an analytical expression of the AUC for the logistic NTCP model in the case of both symmetrical and asymmetrical dose (to the normal tissue) windows around D50. Using numerical simulations, we studied the behavior of the AUC in general clinical settings, enforcing non-logistic NTCP models (Lyman-Kutcher-Burman and LogEUD) and including risk factors beyond the dose. We validated our findings using real-world radiotherapy data sets of prostate cancer patients.

RESULTS

Our analytical expression of the AUC made explicit the dependence on both the steepness of the logistic curve (β) and the dose window width (w), showing that an increase of w pushes AUC towards higher values. Increasing values of the AUC with increasing values of w were consistently observed across simulated data sets with diverse clinical settings from published studies and real clinical data sets.

CONCLUSION

Our results reveal that the AUC of NTCP models inherits intrinsic characteristics from the clinical setting of the data set on which the models are developed, and warn against the use of the AUC to compare the performance of models constructed upon data from trials in which substantially different dose ranges were administered or accounting for different risk factors beyond the dose.

Host Gene Expression to Predict Sepsis Progression

OBJECTIVES

Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.

DESIGN

Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.

PATIENTS

Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.

MEASUREMENTS AND MAIN RESULTS

Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.

CONCLUSIONS

Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

Experiences of COVID-19 infection in North Carolina: A qualitative analysis

Background and aim

It has been demonstrated that marginalized populations across the U.S. have suffered a disproportionate burden of the coronavirus disease 2019 (COVID-19) pandemic, illustrating the role that social determinants of health play in health outcomes. To better understand how these vulnerable and high-risk populations have experienced the pandemic, we conducted a qualitative study to better understand their experiences from diagnosis through recovery.

Methods

We conducted a qualitative study of patients in a North Carolina healthcare system’s registry who tested positive for COVID-19 from March 2020 through February 2021, identified from population-dense outbreaks of COVID-19 (hotspots). We conducted semi-structured phone interviews in English or Spanish, based on patient preference, with trained bilingual study personnel. Each interview was evaluated using a combination of deductive and inductive content analysis to determine prevalent themes related to COVID-19 knowledge, diagnosis, disease experience, and long-term impacts.

Findings

The 10 patients interviewed from our COVID-19 hotspot clusters were of equal distribution by sex, predominantly Black (70%), aged 22–70 years (IQR 45–62 years), and more frequently publicly insured (50% Medicaid/Medicare, vs 30% uninsured, vs 20% private insurance). Major themes identified included prior knowledge of COVID-19 and patient perceptions of their personal risk, the testing process in numerous settings, the process of quarantining at home after a positive diagnosis, the experience of receiving medical care during their illness, and difficulties with long-term recovery.

Discussion

Our findings suggest areas for targeted interventions to reduce COVID-19 transmission in these high-risk communities, as well as improve the patient experience throughout the COVID-19 illness course.

Proposal for a New Pathologic Prognostic Index After Neoadjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma (PINC)

BACKGROUND

Limited information is available on the relevant prognostic variables after surgery for patients with pancreatic ductal adenocarcinoma (PDAC) subjected to neoadjuvant chemotherapy (NACT). NACT is known to induce a spectrum of histological changes in PDAC. Different grading regression systems are currently available; unfortunately, they lack precision and accuracy. We aimed to identify a new quantitative prognostic index based on tumor morphology.

PATIENTS AND METHODS

The study population was composed of 69 patients with resectable or borderline resectable PDAC treated with preoperative NACT (neoadjuvant group) and 36 patients submitted to upfront surgery (upfront-surgery group). A comprehensive histological assessment on hematoxylin and eosin (H&E) stained sections evaluated 20 morphological parameters. The association between patient survival and morphological variables was evaluated to generate a prognostic index.

RESULTS

The distribution of morphological parameters evaluated was significantly different between upfront-surgery and neoadjuvant groups, demonstrating the effect of NACT on tumor morphology. On multivariate analysis for patients that received NACT, the predictors of shorter overall survival (OS) and disease-free survival (DFS) were perineural invasion and lymph node ratio. Conversely, high stroma to neoplasia ratio predicted longer OS and DFS. These variables were combined to generate a semiquantitative prognostic index based on both OS and DFS, which significantly distinguished patients with poor outcomes from those with a good outcome. Bootstrap analysis confirmed the reproducibility of the model.

CONCLUSIONS

The pathologic prognostic index proposed is mostly quantitative in nature, easy to use, and may represent a reliable tumor regression grading system to predict patient outcomes after NACT followed by surgery for PDAC.

The Host Response to Viral Infections Reveals Common and Virus-Specific Signatures in the Peripheral Blood

Viruses cause a wide spectrum of clinical disease, the majority being acute respiratory infections (ARI). In most cases, ARI symptoms are similar for different viruses although severity can be variable. The objective of this study was to understand the shared and unique elements of the host transcriptional response to different viral pathogens. We identified 162 subjects in the US and Sri Lanka with infections due to influenza, enterovirus/rhinovirus, human metapneumovirus, dengue virus, cytomegalovirus, Epstein Barr Virus, or adenovirus. Our dataset allowed us to identify common pathways at the molecular level as well as virus-specific differences in the host immune response. Conserved elements of the host response to these viral infections highlighted the importance of interferon pathway activation. However, the magnitude of the responses varied between pathogens. We also identified virus-specific responses to influenza, enterovirus/rhinovirus, and dengue infections. Influenza-specific differentially expressed genes (DEG) revealed up-regulation of pathways related to viral defense and down-regulation of pathways related to T cell and neutrophil responses. Functional analysis of entero/rhinovirus-specific DEGs revealed up-regulation of pathways for neutrophil activation, negative regulation of immune response, and p38MAPK cascade and down-regulation of virus defenses and complement activation. Functional analysis of dengue-specific up-regulated DEGs showed enrichment of pathways for DNA replication and cell division whereas down-regulated DEGs were mainly associated with erythrocyte and myeloid cell homeostasis, reactive oxygen and peroxide metabolic processes. In conclusion, our study will contribute to a better understanding of molecular mechanisms to viral infections in humans and the identification of biomarkers to distinguish different types of viral infections.