Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Hospital-based proton therapy implementation during the COVID pandemic: early clinical and research experience in a European academic institution

Introduction A rapid deploy of unexpected early impact of the COVID pandemic in Spain was described in 2020. Oncology practice was revised to facilitate decision-making regarding multimodal therapy for prevalent cancer types amenable to multidisciplinary treatment in which the radiotherapy component searched more efcient options in the setting of the COVID-19 pandemic, minimizing the risks to patients whilst aiming to guarantee cancer outcomes. Methods A novel Proton Beam Therapy (PBT), Unit activity was analyzed in the period of March 2020 to March 2021. Institutional urgent, strict and mandatory clinical care standards for early diagnosis and treatment of COVID-19 infection were stablished in the hospital following national health-authorities’ recommendations. The temporary trends of patients care and research projects proposals were registered. Results 3 out of 14 members of the professional staf involved in the PBR intra-hospital process had a positive test for COVID infection. Also, 4 out of 100 patients had positive tests before initiating PBT, and 7 out of 100 developed positive tests along the weekly mandatory special checkup performed during PBT to all patients. An update of clinical performance at the PBT Unit at CUN Madrid in the initial 500 patients treated with PBT in the period from March 2020 to November 2022 registers a distribution of 131 (26%) pediatric patients, 63 (12%) head and neck cancer and central nervous system neoplasms and 123 (24%) re-irradiation indications. In November 2022, the activity reached a plateau in terms of patients under treatment and the impact of COVID pandemic became sporadic and controlled by minor medical actions. At present, the clinical data are consistent with an academic practice prospectively (NCT05151952). Research projects and scientifc production was adapted to the pandemic evolution and its infuence upon professional time availability. Seven research projects based in public funding were activated in this period and preliminary data on molecular imaging guided proton therapy in brain tumors and post-irradiation patterns of blood biomarkers are reported. Conclusions Hospital-based PBT in European academic institutions was impacted by COVID-19 pandemic, although clinical and research activities were developed and sustained. In the post-pandemic era, the benefts of online learning will shape the future of proton therapy education

Focal therapy of prostate cancer index lesion with irreversible electroporation. A prospective study with a median follow-up of 3 years

Purpose: Our aim was to assess oncologic, safety, and quality of lifeerelated outcomes of focal therapy with irreversible electroporation in men with localized prostate cancer. Materials and Methods: This was a single-center, phase II study. Inclusion criteria: prostate cancer International Society of Urological Pathology grade 1-2, prostate specific antigen 15 ng/ml, cT2b. Patients were selected based on multiparametric magnetic resonance imaging and transperineal systematic and targeted magnetic resonance imagingeultrasound fusioneguided biopsy. Ablation of index lesions with safety margin was performed. Primary end point was cancer control, defined as the absence of any biopsy-proven tumor. A control transperineal biopsy was planned at 12 months and when suspected based on prostate specific antigen and/or multiparametric magnetic resonance imaging information. Quality of life was assessed using Expanded Prostate Cancer Index Composite Urinary Continence domain, International Index of Erectile Function, and International Prostate Symptom Score. Results: From November 2014 to July 2021, 41 consecutive patients were included with a median follow-up of 36 months. Thirty patients (73%) had International Society of Urological Pathology grade 1 tumors, 10 (24%) grade 2, and 1 (2.4%) grade 3. Recurrence was observed in 16 of 41 (39%) of the whole cohort, and 16 of 33 (48.4%) who underwent biopsy. In-field recurrence was detected in 5 (15%) and out-of-field in 11 (33.3%). Ten of 41 (24.6%) including 3 of 5 (60%) with in-field recurrences had significant tumors (Gleason pattern 4-5; more than 1 core or any >5 mm involved). Median recurrence-free survival was 32 months (95% CI 6.7-57.2). Twentysix patients (63.4%) were free from salvage treatment. All patients preserved urinary continence. Potency was maintained in 91.8%. Conclusions: Irreversible electroporation can achieve satisfactory 3-year in-field tumor control with excellent quality of life results in selected patients

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients

Background Identifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunotherapy clinical durable benefit, based on the integration of radiomics and clinical data monitored through early anti-PD-1/PD-L1 monoclonal antibodies treatment in patients with advanced non-small cell lung cancer (NSCLC).MethodsIn this study, 264 patients with pathologically confirmed stage IV NSCLC treated with immunotherapy were retrospectively collected from two institutions. The cohort was randomly divided into a training (n = 221) and an independent test set (n = 43), ensuring the balanced availability of baseline and follow-up data for each patient. Clinical data corresponding to the start of treatment was retrieved from electronic patient records, and blood test variables after the first and third cycles of immunotherapy were also collected. Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information.ResultsThe integration of longitudinal clinical and deep-radiomics data significantly improved clinical durable benefit prediction at 6 and 9 months after treatment in the independent test set, achieving an area under the receiver operating characteristic curve of 0.824 (95% CI: [0.658,0.953]) and 0.753 (95% CI: [0.549,0.931]). The Kaplan-Meier survival analysis showed that, for both endpoints, the signatures significantly stratified high- and low-risk patients (p-value< 0.05) and were significantly correlated with progression-free survival (PFS6 model: C-index 0.723, p-value = 0.004; PFS9 model: C-index 0.685, p-value = 0.030) and overall survival (PFS6 models: C-index 0.768, p-value = 0.002; PFS9 model: C-index 0.736, p-value = 0.023).ConclusionsIntegrating multidimensional and longitudinal data improved clinical durable benefit prediction to immunotherapy treatment of advanced non-small cell lung cancer patients. The selection of effective treatment and the appropriate evaluation of clinical benefit are important for better managing cancer patients with prolonged survival and preserving quality of life

Hospital-based proton therapy implementation during the COVID pandemic: early clinical and research experience in a European academic institution

Introduction A rapid deploy of unexpected early impact of the COVID pandemic in Spain was described in 2020. Oncology practice was revised to facilitate decision-making regarding multimodal therapy for prevalent cancer types amenable to multidisciplinary treatment in which the radiotherapy component searched more efcient options in the setting of the COVID-19 pandemic, minimizing the risks to patients whilst aiming to guarantee cancer outcomes. Methods A novel Proton Beam Therapy (PBT), Unit activity was analyzed in the period of March 2020 to March 2021. Institutional urgent, strict and mandatory clinical care standards for early diagnosis and treatment of COVID-19 infection were stablished in the hospital following national health-authorities’ recommendations. The temporary trends of patients care and research projects proposals were registered. Results 3 out of 14 members of the professional staf involved in the PBR intra-hospital process had a positive test for COVID infection. Also, 4 out of 100 patients had positive tests before initiating PBT, and 7 out of 100 developed positive tests along the weekly mandatory special checkup performed during PBT to all patients. An update of clinical performance at the PBT Unit at CUN Madrid in the initial 500 patients treated with PBT in the period from March 2020 to November 2022 registers a distribution of 131 (26%) pediatric patients, 63 (12%) head and neck cancer and central nervous system neoplasms and 123 (24%) re-irradiation indications. In November 2022, the activity reached a plateau in terms of patients under treatment and the impact of COVID pandemic became sporadic and controlled by minor medical actions. At present, the clinical data are consistent with an academic practice prospectively (NCT05151952). Research projects and scientifc production was adapted to the pandemic evolution and its infuence upon professional time availability. Seven research projects based in public funding were activated in this period and preliminary data on molecular imaging guided proton therapy in brain tumors and post-irradiation patterns of blood biomarkers are reported. Conclusions Hospital-based PBT in European academic institutions was impacted by COVID-19 pandemic, although clinical and research activities were developed and sustained. In the post-pandemic era, the benefts of online learning will shape the future of proton therapy education