Search

Background and Objective: Combinations of VEGFR-TKIs plus ICI against PD1/PD-L1 are the standard first-line therapy for patients with mRCC, irrespective of the prognostic class. This study aims to investigate the feasibility and safety of withdrawing the VEGFR-TKI but continuing the anti- PD1/PD-L1 in patients who achieve response to their combination., Methods: This was a single-arm phase 2 trial in patients with treatment naïve mRCC with prior nephrectomy, without symptomatic/bulky disease and no liver metastases. Enrolled patients received axitinib+avelumab, after 36 weeks of therapy those who achieved tumor response interrupted axitinib and continued avelumab maintenance until disease progression. The primary endpoint was the rate of patients without progression eight weeks after the axitinib interruption. Secondary endpoints were the median value for progression free (mPFS) and overall survival (mOS) and the safety in the overall population., Key Findings and Limitations: 79 patients were enrolled and 75 evaluated for efficacy. A total of 29 (38%) patients had axitinib withdrawn, as per the study design, with 72% of them having no progression after eight weeks and thus achieving the primary endpoint. The mPFS of the overall population was 24 months while the mOS was not reached. The ORR was 76% (12% CR, 64% PR), with 19% of patients having stable disease. In the patients who discontinued axitinib, the incidence of AEs of any grade was 59% and 3% for grade 3 or 4. This study was limited by the lack of the comparative arm., Conclusions and Clinical Implications: The TIDE-A study demonstrates that the withdrawal of VEGFR-TKI with ICI maintenance is feasible for selected mRCC patients with evidence of response to the VEGFR-TKI+ICI combination employed in first-line therapy. Axitinib interruption with avelumab maintenance led to decreased side effects and should be further investigated as a new strategy to delay tumor progression., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Immunotherapy represented a turning point for treating extensive small-cell lung cancer (ES-SCLC). Although, many issues remain debated., Methods: A group of Italian medical and radiation oncologists with expertise in managing patients with ES-SCLC developed a list of statements divided in six areas of interest. The Delphi method was used to assess the consensus on the defined list of statements., Results: 32 statements were included in the final list to be voted by the Delphi panel, and 26 reached a consensus on the agreement. A prompt involvement of a multidisciplinary team is a priority to provide an integrated treatment strategy. First-line recommended treatment is immunotherapy in combination with platinum-based chemotherapy and etoposide for four cycles followed by maintenance immunotherapy., Conclusions: While awaiting new data from clinical trials and real-world studies, these recommendations can represent a useful tool to guide the management of ES-SCLC patients in daily practice., Competing Interests: Declaration of Competing Interest Francesco Agustoni received a grant for an advisory role from Roche. Lucia Bonomi received honoraria from Bristol-Myers Squibb/Celgene, MSD Oncology, AstraZeneca, Astellas Pharma, Janssen Oncology; fee for consulting or advisory roles from Janssen Oncology, Ipsen, Roche. Paolo Borghetti received honoraria from AstraZeneca, Roche. Giovanni Luca Ceresoli received fees for speaker engagements from Bristol Myers Squibb, Merck Sharp & Dohme, Novocure, AstraZeneca, Bayer, and Astellas; and fees for advisory roles from Bristol Myers Squibb, Novocure, and AstraZeneca. Andrea Riccardo Filippi received fees as speakers’ bureau from Astra Zeneca, MSD, Roche, Ipsen; fees for advisory role from Astra Zeneca, Roche; and research funding from Astra Zeneca. He also participated (no financial interest) in sponsored research from Astra Zeneca, Roche, MSD. Giovanna Finocchiaro received personal fees for speaker engagements from AstraZeneca and for advisory roles from MSD, and AMGEN. Letizia Gianoncelli received personal fees for speaker engagements from AstraZeneca, Bristol Myers Squibb, MSD, and Roche. Salvatore Grisanti received a fee for an advisory board from Roche. Fabio Pagni received consulting or advisory fees from Lilly, Amgen, Roche, MSD, Novartis, and Janssen. Prof Pagni participated in the paper during his involvement as PI of the Italian MUR Dipartimenti di Eccellenza 2023–2027 (l. 232/2016, art. 1, comma 314 - 337). Claudia Proto received consulting or advisory fees from AstraZeneca, Roche, MSD, BMS, and Janssen; research funding from Roche, AstraZeneca, Pfizer, Celgene, MSD, BMS, Daichii; fees for travel, accommodation, and expenses from AstraZeneca, Roche, and MSD. Erika Rijavec received honoraria from Bristol-Myers Squibb, AstraZeneca MSD, and Roche; advisory board fees from Sanofi; and travel grants from Daichii Sankyo. All other authors have no conflicts of interest to declare., (Copyright © 2024. Published by Elsevier B.V.)

Data synthesis can address important data availability challenges in biomedical informatics. Quantitative evaluation of generative models may help understand their applications to synthesizing biomedical data. This poster paper examines state-of-the-art generative models used in medical imaging, such as StyleGAN and DDPM models, and evaluates their performance in learning data manifolds and in the visible features of generated samples. Results show that existing generative models have much to improve based on the studied measures.

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results.

Background: Age-, gender- and body site-specific values of thermal Quantitative Sensory Testing (QST) measures have not yet been reported using the novel and cheap device 'Q-sense'. Here, we aimed to assess normative values of Q-sense-derived parameters in a representative Italian population., Methods: QST parameters were measured in 84 healthy participants (42 males; aged 20-76 years) equally distributed into three age groups (18-39, 40-59 and 60-80 years). We explored the Warm and the Cold Detection Thresholds (WDT and CDT, respectively) with the method of limits (MLI) and the method of levels (MLE), and the Heat Pain Threshold (HPT) with the MLI. We tested the trigeminal supraorbital region, the hand thenar, and the foot dorsum on the right body side., Results: We calculated non-parametric reference limits (2.5-97.5 th ) according to age, gender and tested site. All QST measures were affected by age, gender and tested site. In the extra-trigeminal body sites, females showed lower WDT and higher CDT, while males had higher HPT. Worse sensory discriminative abilities and increased HPT values were found in people aged over 40 on the foot. Age-related differences were more evident with the reaction time-dependent MLI vs. MLE paradigm., Conclusions: Demographic characteristics must be considered when QST is used in the clinical setting. The definition of reference limits for sensory testing with the Q-sense herein provided can pave the way towards a more widespread use of thermal QST for diagnosing small fiber neuropathy and for identifying patients' profiles in different chronic pain syndromes., (© 2023. The Author(s).)

Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel distances to health facilities is vital to policy making and designing effective interventions. However, sharing these data may raise privacy concerns, as information about individual data contributors (e.g., health status and residential address) may be disclosed. In this work, we investigate those unintended information leakage in spatial accessibility analysis. Specifically, we are interested in understanding whether sharing data for spatial accessibility computations may disclose individual participation (i.e., membership inference) and personal identifiable information (i.e., address inference). Furthermore, we propose two provably private algorithms that mitigate those privacy risks. The evaluation is conducted with real population and healthcare facilities data from Mecklenburg county, NC and Nashville, TN. Compared to state-of-the-art privacy practices, our methods effectively reduce the risks of membership and address disclosure, while providing useful data for spatial accessibility analysis.

Sharing health data is vital in advancing medical research and transforming knowledge into clinical practice. Meanwhile, protecting the privacy of data contributors is of paramount importance. To that end, several privacy approaches have been proposed to protect individual data contributors in data sharing, including data anonymization and data synthesis techniques. These approaches have shown promising results in providing privacy protection at the dataset level. In this work, we study the privacy challenges in enabling fine-grained privacy in health data sharing. Our work is motivated by recent research findings, in which patients and healthcare providers may have different privacy preferences and policies that need to be addressed. Specifically, we propose a novel and effective privacy solution that enables data curators (e.g., healthcare providers) to protect sensitive data elements while preserving data usefulness. Our solution builds on randomized techniques to provide rigorous privacy protection for sensitive elements and leverages graphical models to mitigate privacy leakage due to dependent elements. To enhance the usefulness of the shared data, our randomized mechanism incorporates domain knowledge to preserve semantic similarity and adopts a block-structured design to minimize utility loss. Evaluations with real-world health data demonstrate the effectiveness of our approach and the usefulness of the shared data for health applications.

Deep neural networks have been increasingly integrated in healthcare applications to enable accurate predicative analyses. Sharing trained deep models not only facilitates knowledge integration in collaborative research efforts but also enables equitable access to computational intelligence. However, recent studies have shown that an adversary may leverage a shared model to learn the participation of a target individual in the training set. In this work, we investigate privacy-protecting model sharing for survival studies. Specifically, we pose three research questions. (1) Do deep survival models leak membership information? (2) How effective is differential privacy in defending against membership inference in deep survival analyses? (3) Are there other effects of differential privacy on deep survival analyses? Our study assesses the membership leakage in emerging deep survival models and develops differentially private training procedures to provide rigorous privacy protection. The experimental results show that deep survival models leak membership information and our approach effectively reduces membership inference risks. The results also show that differential privacy introduces a limited performance loss, and may improve the model robustness in the presence of noisy data, compared to non-private models.

Objectives: The selective RET-inhibitor pralsetinib has shown therapeutic activity in early clinical trials in patients with non-small cell lung cancer (NSCLC) harboring rearranged during transfection (RET) gene fusions. To date, the real-world efficacy of pralsetinib in this population is unknown., Materials and Methods: A retrospective efficacy and safety analysis was performed on data from patients with RET-fusion positive NSCLC enrolled in the pralsetinib Italian expanded access program between July 2019 and October 2021., Results: Overall, 62 patients with RET-fusion positive NSCLC received pralsetinib at 20 Italian centers. Next-generation sequencing was used to detect RET alterations in 44 patients (73 %). The most frequent gene fusion partner was KIF5B (75 % of 45 evaluable). Median age was 62 years (range, 36-90), most patients were female (57 %) and never smokers (53 %). Brain metastases were known in 18 patients (29.5 %) at the time of pralsetinib treatment. 13 patients were treatment naïve (unfit for chemotherapy), 48 were pretreated (median number of previous lines: 1, range, 1-4). The objective response rate (ORR) was 66 % [95 % confidence interval (CI), 53-81] in the evaluable population (n = 59). The disease control rate (DCR) was 79 %. After a median follow-up of 10.1 months, the median progression free survival was 8.9 months (95 %CI, 4.7-NA). In patients with measurable brain metastases (n = 6) intracranial ORR was 83 %, intracranial DCR was 100 %. Overall, 83.6 % of patients experienced any-grade treatment-related adverse events (TRAEs), 39 % grade 3 or greater (G ≥ 3). The most common G ≥ 3 TRAEs were neutropenia (9.8 %), dry mouth/oral mucositis (8.2 %), and thrombocytopenia (6.6 %). Seven patients (12 %) discontinued pralsetinib due to TRAEs, twenty-six had at least one dose level modification due to TRAEs. Two treatment-related deaths were observed (1 sepsis, 1 typhlitis)., Conclusions: In the real-world setting, pralsetinib confirmed durable systemic activity and intracranial response in RET-fusion positive NSCLC. Toxicity profile was consistent with previous reports., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective: Emerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information, posing new privacy challenges. In this work, we study the applicability of a novel privacy model to enable individual-level time-series data sharing while maintaining the usability for data analytics., Methods and Materials: We propose a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data, which leverages dimensional reduction technique and random sampling to achieve provable privacy protection. We show that our solution provides strong privacy protection against an informed adversarial model while enabling useful aggregate-level analysis., Results: We conduct our evaluations on 2 real-world ECG datasets. Our empirical results show that the privacy risk is significantly reduced after sanitization while the data usability is retained for a variety of clinical tasks (eg, predictive modeling and clustering)., Discussion: Our study investigates the privacy risk in sharing individual-level ECG time-series data. We demonstrate that individual-level data can be highly unique, requiring new privacy solutions to protect data contributors., Conclusion: The results suggest our proposed privacy-protection method provides strong privacy protections while preserving the usefulness of the data., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Sharing time-to-event data is beneficial for enabling collaborative research efforts (e.g., survival studies), facilitating the design of effective interventions, and advancing patient care (e.g., early diagnosis). Despite numerous privacy solutions for sharing time-to-event data, recent research studies have shown that external information may become available (e.g., self-disclosure of study participation on social media) to an adversary, posing new privacy concerns. In this work, we formulate a cohort inference attack for time-to-event data sharing, in which an informed adversary aims at inferring the membership of a target individual in a specific cohort. Our study investigates the privacy risks associated with time-to-event data and evaluates the empirical privacy protection offered by popular privacy-protecting solutions (e.g., binning, differential privacy). Furthermore, we propose a novel approach to privately release individual level time-to-event data with high utility, while providing indistinguishability guarantees for the input value. Our method TE-Sanitizer is shown to provide effective mitigation against the inference attacks and high usefulness in survival analysis. The results and discussion provide domain experts with insights on the privacy and the usefulness of the studied methods.

The use of deep learning techniques in medical applications holds great promises for advancing health care. However, there are growing privacy concerns regarding what information about individual data contributors (i.e., patients in the training set) these deep models may reveal when shared with external users. In this work, we first investigate the membership privacy risks in sharing deep learning models for cancer genomics tasks, and then study the applicability of privacy-protecting strategies for mitigating these privacy risks.

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX.

Purpose: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research., Design: Development and evaluation of machine learning models., Methods: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall., Results: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests)., Conclusions: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research., (Published by Elsevier Inc.)

Introduction: In Europe, the SARS-CoV-2 pandemic had its first epicenter in Italy. Despite a significant mortality rate, the severity of most cases of COVID-19 infection ranges from asymptomatic to mildly symptomatic, and silent infection affects a still-unknown proportion of the general population. No information is available on the prevalence and clinical impact of SARS-CoV-2 silent infection among patients with cancer receiving anticancer treatment during the pandemic., Materials and Methods: From April 1, 2020, to the end of the same month, 560 consecutive patients with cancer, asymptomatic for COVID-19 and on anticancer treatment at Papa Giovanni XXIII Hospital in Bergamo, were evaluated and tested for SARS-CoV-2. We implemented a two-step diagnostics, including the rapid serological immunoassay for anti-SARS-CoV-2 immunoglobulin (Ig) G/IgM and the nasopharyngeal swab reverse transcriptase-polymerase chain reaction (RT-PCR) test in case of seropositivity to identify SARS-CoV-2 silent carriers., Results: In 560 patients, 172 (31%) resulted positive for anti-SARS-CoV-2 IgM/IgG antibodies, regardless of different type of cancer, stage, and treatment. The Ig-seropositive patients were then tested with RT-PCR nasopharyngeal swabs, and 38% proved to be SARS-CoV-2 silent carriers. At an early follow-up, in the 97 SARS-CoV-2-seropositive/RT-PCR-negative patients who continued their anticancer therapies, only one developed symptomatic COVID-19 illness., Conclusion: Among patients with cancer, the two-step diagnostics is feasible and effective for SARS-CoV-2 silent carriers detection and might support optimal cancer treatment strategies at both the individual and the population level. The early safety profile of the different anticancer therapies, in patients previously exposed to SARS-CoV-2, supports the recommendation to continue the active treatment, at least in cases of RT-PCR-negative patients., Implications for Practice: This is the first study evaluating the prevalence and clinical impact of SARS-CoV-2 silent infection in actively treated patients with cancer, during the epidemic peak in one of the worst areas of the COVID-19 pandemic. Lacking national and international recommendations for the detection of asymptomatic SARS-CoV-2 infection, a pragmatic and effective two-step diagnostics was implemented to ascertain SARS-CoV-2 silent carriers. In this series, consisting of consecutive and unselected patients with cancer, the prevalence of both SARS-CoV-2-seropositive patients and silent carriers is substantial (31% and 10%, respectively). The early safety profile of the different anticancer therapies, in patients previously exposed to SARS-CoV-2, supports the recommendation to continue the active treatment, at least in case of RT-PCR-negative patients., (© 2020 AlphaMed Press.)

Background: In ambulatory patients with cancer with asymptomatic or pauci-symptomatic SARS-CoV-2 infection, the safety of targeted therapies (TTs), chemotherapy (CT) or immune checkpoint inhibitors (ICIs) therapy is still unknown., Material and Methods: From the start of the first epidemic wave of SARS-CoV-2 in Bergamo, Italy, we have prospectively screened all consecutive outpatients who presented for treatment to the Oncology Division of the Papa Giovanni XXIII Hospital, Bergamo for SARS-CoV-2 antigen expression. We identified patients treated with ICIs and compared these to patients with the same cancer subtypes treated with TTs or CT., Results: Between March 5 and May 18, 293 consecutive patients (49% melanoma, 34% non-small cell lung cancer, 9% renal cell carcinoma, 8% other) were included in this study: 159 (54%), 50 (17%) and 84 (29%) received ICIs, CT or TTs, respectively. Overall 89 patients (30.0%) were SARS-CoV-2 positive. Mortality of SARS-CoV-2-positive patients was statistically significantly higher compared with SARS-CoV-2 negative patients (8/89 vs 3/204, respectively, Fisher's exact test p=0.004). All deaths were due to COVID-19. Serious adverse events (SAEs) were more frequent in SARS-CoV-2-positive patients compared with SARS-CoV-2-negative cases (Cochran-Mantel-Haenszel (CMH) test p=0.0008). The incidence of SAEs in SARS-CoV-2 positive compared with SARS-CoV-2 negative patients was similar in ICI and CT patients (17.3% and 3.7% for positive and negative patients in ICIs and 15.4% and 2.7% in CT, Breslow-Day test p=0.891). No COVID-19-related SAEs were observed in the TTs patients., Conclusions: The incidence of SAEs was higher for SARS-CoV-2-positive patients treated with ICIs and CT, mostly in advanced disease. No SAEs were observed in patients treated with TTs. SAEs were COVID-19 related rather than treatment related. Treatment with ICIs does not appear to significantly increase risk of SAEs compared with CT. This information should be considered when determining treatment options for patients., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Temporal medical data are increasingly integrated into the development of data-driven methods to deliver better healthcare. Searching such data for patterns can improve the detection of disease cases and facilitate the design of preemptive interventions. For example, specific temporal patterns could be used to recognize low-prevalence diseases, which are often under-diagnosed. However, searching these patterns in temporal medical data is challenging, as the data are often noisy, complex, and large in scale. In this work, we propose an effective and efficient solution to search for patients who exhibit conditions that resemble the input query. In our solution, we propose a similarity notion based on the Longest Common Subsequence (LCSS), which is used to measure the similarity between the query and the patient's temporal medical data and to ensure robustness against noise in the data. Our solution adopts locality sensitive hashing techniques to address the high dimensionality of medical data, by embedding the recorded clinical events (e.g., medications and diagnosis codes) into compact signatures. To perform pattern search in large EHR datasets, we propose a filtering approach based on tandem patterns, which effectively identifies candidate matches while discarding irrelevant data. The evaluations conducted using a real-world dataset demonstrate that our solution is highly accurate while significantly accelerating the similarity search., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Meningeal carcinomatosis is rare in patients with kidney cancer and treatment options are limited. Few patients treated with systemic approaches have been reported. We describe a case of complete remission of leptomeningeal metastasis in a patient with renal cell carcinoma treated with nivolumab. To our knowledge, this is the first report of nivolumab safety and efficacy in this particular site of metastasis., Case Presentation: Our patient was a 60-year-old Caucasian man with bone and lung metastases from renal cell carcinoma. He developed leptomeningeal metastasis and progression of bone and lung lesions after only 2 months of his first-line treatment. He was then treated with nivolumab in second-line setting and experienced a rapid improvement of cancer-related symptoms, complete remission of leptomeningeal and lung lesions, and increased bone mineral density in bone metastasis. The patient did not experience any drug-related toxicity., Conclusions: Meningeal carcinomatosis metastasis from renal cancer is a rare condition. Diagnosis is often challenging: the onset of nonspecific presenting symptoms could be initially attributed to bone involvement, side effects of oncologic therapy, or paraneoplastic syndromes. Our case suggests that nivolumab could be an effective and safe treatment option in patients with pretreated renal cancer with leptomeningeal metastasis.