IRIS UniSR (’Università Vita-Salute San Raffaele)
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Factors associated with reaching maintenance therapy in patients with advanced biliary tract cancer treated with durvalumab: Real-world results from a multicenter and multinational study
No full textStandard of care first-line systemic treatment for advanced biliary tract cancer includes chemo-immunotherapy with gemcitabine, cisplatin, and durvalumab, followed by maintenance durvalumab monotherapy. The present work aims to investigate the differences in baseline clinical and molecular characteristics between patients with early progression during chemo-immunotherapy and those who reach durvalumab maintenance therapy. The study population included patients with unresectable, locally advanced, or metastatic BTC who received treatment at 38 clinical Institutions in 12 countries from July 2021 to December 2023. The primary objective of the study was to investigate whether baseline clinical and molecular characteristics differed between patients with early progression during chemo-immunotherapy versus those reaching durvalumab maintenance therapy. Four hundred forty-eight patients were included in this study. Two hundred twenty-seven patients (50.7%) received maintenance with durvalumab monotherapy, whereas 221 (49.3%) did not receive maintenance therapy due to PD during first-line chemo-immunotherapy before completing 8 cycles. Results show that patients who received maintenance were more likely to be older (≥70 years), have an ECOG = 0, locally advanced disease, and a neutrophil-to-lymphocyte ratio (NLR) <3. A higher proportion of patients with BAP1 mutations received maintenance, while TP53 mutations were more common in those who progressed early. According to the present analysis, a substantial proportion of patients (50.7%) with advanced BTC who were treated with chemotherapy plus durvalumab proceeded to receive maintenance therapy with durvalumab monotherapy, with a median treatment duration of 4.4 cycles. Patients ≥70 years, with ECOG PS 0, with locally advanced disease, and with NLR <3 had a higher likelihood of receiving maintenance therapy
Full-cycle MSCT analysis for a less restrictive Neo-LVOT area cut-off in transcatheter mitral valve-in-valve
No full textFrege meets Belnap: Basic Law V in a Relevant Logic
No full textAbstractionism in the philosophy of mathematics aims at deriving large fragments of mathematics by combining abstraction principles (i.e. the abstract objects §e1,§e2, are identical if, and only if, an equivalence relation Eq§ holds between the entities e1,e2) with logic. Still, as highlighted in work on the semantics for relevant logics, there are different ways theories might be combined. In exactly what ways must logic and abstraction be combined in order to get interesting mathematics? In this paper, we investigate the matter by deriving the axioms of second-order Peano Arithmetic from Frege’s Basic Law V (the extension of F is identical with the extension of G if, and only if, F and G are extensionally equivalent) in the presence of a relevant higher-order logic. The results are interesting. Not only must we take on logic as true, and not only must we apply our logic to abstraction principles, but also we have to apply our theory of abstraction back to the logic in order to arrive at arithmetic. Thus, what Abstractionism gives us is not simply what we get from abstraction via logic, but also what we get from logic via abstraction
Current management of hepatobiliary malignancies between centers with or without a liver transplant program: A multi-society national survey
No full textBackground: Availability of liver transplantation (LT) as a treatment for hepatocellular carcinoma (HCC) and other liver malignancies may determine heterogeneity of therapeutic strategies across different centers. Aims: To investigate the practice between hepato-biliary centers without (HB centers) and with a LT program (LT centers), we launched a 38-item web-based national survey, with directors of centers as a target. Methods: The survey, including 4 clinical vignettes, collected data on their approach to HCC and transplant oncology. Results: After duplicates removal, 75 respondents were considered. Respondents from LT centers (n = 22, 29.3 %) were more in favor of LT in the case of HCC outside Milan criteria (90.9 % vs. 67.9 %, p = 0.037), recurrent HCC (95.5 % vs. 50.9 %, p = 0.002) and other malignancies such as cholangiocarcinoma or neuroendocrine tumors. No significant difference was observed concerning the proportion of centers favorable to LT for unresectable colorectal liver metastases (100 % vs. 88.7 %, p = 0.100). Conclusion: This national survey showed how management of HCC and awareness of transplant oncology may differ between HB and LT centers. Effective networking between HB and LT centers is crucial to provide optimal treatment and access to LT.Background: Availability of liver transplantation (LT) as a treatment for hepatocellular carcinoma (HCC) and other liver malignancies may determine heterogeneity of therapeutic strategies across different centers. Aims: To investigate the practice between hepato-biliary centers without (HB centers) and with a LT program (LT centers), we launched a 38-item web-based national survey, with directors of centers as a target. Methods: The survey, including 4 clinical vignettes, collected data on their approach to HCC and transplant oncology. Results: After duplicates removal, 75 respondents were considered. Respondents from LT centers (n = 22, 29.3 %) were more in favor of LT in the case of HCC outside Milan criteria (90.9 % vs. 67.9 %, p = 0.037), recurrent HCC (95.5 % vs. 50.9 %, p = 0.002) and other malignancies such as cholangiocarcinoma or neuroendocrine tumors. No significant difference was observed concerning the proportion of centers favorable to LT for unresectable colorectal liver metastases (100 % vs. 88.7 %, p = 0.100). Conclusion: This national survey showed how management of HCC and awareness of transplant oncology may differ between HB and LT centers. Effective networking between HB and LT centers is crucial to provide optimal treatment and access to LT
Decoding glioma molecular heterogeneity by advanced radiomic and radiogenomic analyses
No full textHigh-grade gliomas (HGGs) are the most common primary brain malignancy. One of their main characteristics is a great inter and intra-tumor molecular, cellular, and histological heterogeneity. In particular, the molecular characterization of HGGs has gained importance in neuro-oncology, as it enables more precise prognostic stratification than histology alone. However, HGGs diagnosis still relies on histopathological analyses of surgical specimens, which do not fully represent the entire tumor. This highlights the need for reliable, non-invasive biomarkers for comprehensive disease assessment and dynamic monitoring. Magnetic resonance imaging (MRI), including conventional (cMRI) and advanced (aMRI) sequences, provides a macroscopic perspective on HGG heterogeneity, serving as an imaging biomarker. Quantitative features derived from cMRI and aMRI can be integrated in radiomic and radiogenomic models to uncover distinct sub-visual imaging patterns with clinical and molecular relevance.
The aim of this thesis was to decode glioma heterogeneity by advanced radiomic and radiogenomic analyses through three experiments.
In the first experiment, I applied a novel habitat imaging approach to characterize HGGs heterogeneity, integrating information on Hypoxia, advanced PERfusion, and DIffusion MRI techniques together with amino acid PET in a unique HYPERDIrect map. I identified a robust and reproducible pattern of habitats’ distribution and a strong correlation between habitats’ predicted characteristics and histological, immunohistochemical, and molecular tissue properties. Furthermore, I evaluated the potential use of the HYPERDIrect map as a clinical predictor, finding an interesting correlation between habitats’ volumes and patient prognosis.
In the second experiment, I conducted a structured analysis of the interplay between MRI-derived metrics and circulating cell-free DNA (ccfDNA) levels and their potential integration into machine-learning models to predict patient prognosis. I observed a weak correlation between these two variables at single time points but a strong relationship in longitudinal analyses, highlighting the potential use of ccfDNA for HGGs follow-up. Interestingly, radiomic features improved the prediction of ccfDNA levels compared to tumor volume alone, suggesting that factors related to tumor heterogeneity significantly influence the amount of tumor-related cfDNA released in the bloodstream. Finally, the integration of radiomic features and ccfDNA outperformed ccfDNA alone in predicting progression-free survival.
In the third experiment, I investigated how subvisual quantitative imaging features of gliomas relate to their molecular status. I defined a robust pipeline to develop a radiogenomic model capable of correctly classifying lesions according to IDH-molecular status with high accuracy, specificity, and sensitivity. Additionally, I evaluated the radiological significance of the extracted radiomic features. Finally, I developed a radiomic model incorporating the same features along with clinical variables, which demonstrates strong performance in identifying patients with a worse prognosis.
In conclusion, these experiments show different imaging biomarkers directly related to the heterogeneity, molecular features, and clinical behavior of gliomas. The findings of this thesis highlight the significant potential of non-invasive imaging biomarkers, alone or in combination with other biological markers, to provide a comprehensive, tumor-wide, and dynamic characterization of gliomas, making them highly valuable tools for patient stratification and clinical evaluation.High-grade gliomas (HGGs) are the most common primary brain malignancy. One of their main characteristics is a great inter and intra-tumor molecular, cellular, and histological heterogeneity. In particular, the molecular characterization of HGGs has gained importance in neuro-oncology, as it enables more precise prognostic stratification than histology alone. However, HGGs diagnosis still relies on histopathological analyses of surgical specimens, which do not fully represent the entire tumor. This highlights the need for reliable, non-invasive biomarkers for comprehensive disease assessment and dynamic monitoring. Magnetic resonance imaging (MRI), including conventional (cMRI) and advanced (aMRI) sequences, provides a macroscopic perspective on HGG heterogeneity, serving as an imaging biomarker. Quantitative features derived from cMRI and aMRI can be integrated in radiomic and radiogenomic models to uncover distinct sub-visual imaging patterns with clinical and molecular relevance.
The aim of this thesis was to decode glioma heterogeneity by advanced radiomic and radiogenomic analyses through three experiments.
In the first experiment, I applied a novel habitat imaging approach to characterize HGGs heterogeneity, integrating information on Hypoxia, advanced PERfusion, and DIffusion MRI techniques together with amino acid PET in a unique HYPERDIrect map. I identified a robust and reproducible pattern of habitats’ distribution and a strong correlation between habitats’ predicted characteristics and histological, immunohistochemical, and molecular tissue properties. Furthermore, I evaluated the potential use of the HYPERDIrect map as a clinical predictor, finding an interesting correlation between habitats’ volumes and patient prognosis.
In the second experiment, I conducted a structured analysis of the interplay between MRI-derived metrics and circulating cell-free DNA (ccfDNA) levels and their potential integration into machine-learning models to predict patient prognosis. I observed a weak correlation between these two variables at single time points but a strong relationship in longitudinal analyses, highlighting the potential use of ccfDNA for HGGs follow-up. Interestingly, radiomic features improved the prediction of ccfDNA levels compared to tumor volume alone, suggesting that factors related to tumor heterogeneity significantly influence the amount of tumor-related cfDNA released in the bloodstream. Finally, the integration of radiomic features and ccfDNA outperformed ccfDNA alone in predicting progression-free survival.
In the third experiment, I investigated how subvisual quantitative imaging features of gliomas relate to their molecular status. I defined a robust pipeline to develop a radiogenomic model capable of correctly classifying lesions according to IDH-molecular status with high accuracy, specificity, and sensitivity. Additionally, I evaluated the radiological significance of the extracted radiomic features. Finally, I developed a radiomic model incorporating the same features along with clinical variables, which demonstrates strong performance in identifying patients with a worse prognosis.
In conclusion, these experiments show different imaging biomarkers directly related to the heterogeneity, molecular features, and clinical behavior of gliomas. The findings of this thesis highlight the significant potential of non-invasive imaging biomarkers, alone or in combination with other biological markers, to provide a comprehensive, tumor-wide, and dynamic characterization of gliomas, making them highly valuable tools for patient stratification and clinical evaluation
TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury
No full textPractices for controlling intracranial pressure (ICP) in traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) vary considerably between centres. To help understand the rational basis for such variance in care, this study aims to identify the patient-level predictors of changes in ICP management. We extracted all heterogeneous data (2008 pre-ICU and ICU variables) collected from a prospective cohort (n = 844, 51 ICUs) of ICP-monitored TBI patients in the Collaborative European NeuroTrauma Effectiveness Research in TBI study. We developed the TILTomorrow modelling strategy, which leverages recurrent neural networks to map a token-embedded time series representation of all variables (including missing values) to an ordinal, dynamic prediction of the following day’s five-category therapy intensity level (TIL(Basic)) score. With 20 repeats of fivefold cross-validation, we trained TILTomorrow on different variable sets and applied the TimeSHAP (temporal extension of SHapley Additive exPlanations) algorithm to estimate variable contributions towards predictions of next-day changes in TIL(Basic). Based on Somers’ Dxy, the full range of variables explained 68% (95% CI 65–72%) of the ordinal variation in next-day changes in TIL(Basic) on day one and up to 51% (95% CI 45–56%) thereafter, when changes in TIL(Basic) became less frequent. Up to 81% (95% CI 78–85%) of this explanation could be derived from non-treatment variables (i.e., markers of pathophysiology and injury severity), but the prior trajectory of ICU management significantly improved prediction of future de-escalations in ICP-targeted treatment. Whilst there was no significant difference in the predictive discriminability (i.e., area under receiver operating characteristic curve) between next-day escalations (0.80 [95% CI 0.77–0.84]) and de-escalations (0.79 [95% CI 0.76–0.82]) in TIL(Basic) after day two, we found specific predictor effects to be more robust with de-escalations. The most important predictors of day-to-day changes in ICP management included preceding treatments, age, space-occupying lesions, ICP, metabolic derangements, and neurological function. Serial protein biomarkers were also important and may serve a useful role in the clinical armamentarium for assessing therapeutic needs. Approximately half of the ordinal variation in day-to-day changes in TIL(Basic) after day two remained unexplained, underscoring the significant contribution of unmeasured factors or clinicians’ personal preferences in ICP treatment. At the same time, specific dynamic markers of pathophysiology associated strongly with changes in treatment intensity and, upon mechanistic investigation, may improve the timing and personalised targeting of future care
Blockade of αvβ6 and αvβ8 integrins with a chromogranin A-derived peptide inhibits TGFβ activation in tumors and suppresses tumor growth
Get PDFBackgroundThe alpha v beta 6- and alpha v beta 8-integrins, two cell-adhesion receptors upregulated in many solid tumors, can promote the activation of transforming growth factor-beta (TGF beta), a potent immunosuppressive cytokine, by interacting with the RGD sequence of the latency-associated peptide (LAP)/TGF beta complex. We have previously described a chromogranin A-derived peptide, called "peptide 5a", which recognizes the RGD-binding site of both alpha v beta 6 and alpha v beta 8 with high affinity and selectivity, and efficiently accumulates in alpha v beta 6- or alpha v beta 8-positive tumors. This study aims to demonstrate that peptide 5a can inhibit TGF beta activation in tumors and suppress tumor growth.MethodsPeptide 5a was chemically coupled to human serum albumin (HSA) to prolong its plasma half-life. The integrin recognition properties of this conjugate (called 5a-HSA) and its capability to block TGF beta activation by alpha v beta 6+ and/or alpha v beta 8+ cancer cells or by regulatory T cells (Tregs) were tested in vitro. The in vivo anti-tumor effects of 5a-HSA, alone and in combination with S-NGR-TNF (a vessel-targeted derivative of tumor necrosis factor-a), were investigated in various murine tumor models, including pancreatic ductal adenocarcinoma, fibrosarcoma, prostate cancer, and mammary adenocarcinoma.ResultsIn vitro assays showed that peptide 5a coupled to HSA maintains its capability of recognizing alpha v beta 6 and alpha v beta 8 with high affinity and selectivity and inhibits TGF beta activation mediated by alpha v beta 6+ and/or alpha v beta 8+ cancer cells, as well as by alpha v beta 8+ Tregs. In vivo studies showed that systemic administration of 5a-HSA to tumor-bearing mice can reduce TGF beta signaling in neoplastic tissues and promote CD8-dependent anti-tumor responses. Combination therapy studies showed that 5a-HSA can enhance the anti-tumor activity of S-NGR-TNF, leading to tumor eradication.ConclusionPeptide 5a is an efficient tumor-homing inhibitor of alpha v beta 6- and alpha v beta 8-integrin that after coupling to HSA, can be used as a drug to block integrin-dependent TGF beta activation in tumors and promote immunotherapeutic responses.Graphical AbstractThe 5a-HSA conjugate, a compound consisting of the chromogranin A-derived peptide 5a coupled to human serum albumin (HSA), can bind the RGD binding site of alpha v beta 6 and alpha v beta 8 integrins expressed by tumor cells and tumor-infiltrating regulatory T cells (Tregs) and inhibits alpha v beta 6- and/or alpha v beta 8-mediated activation of TGF beta, thereby reducing its immunosuppressive effects and promoting anti-tumor immune response
Upfront surgery for intrahepatic cholangiocarcinoma: Prediction of futility using artificial intelligence
No full textObjective: We sought to identify patients at risk of “futile” surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)–based model based on preoperative variables. Methods: Intrahepatic cholangiocarcinoma patients who underwent resection between 1990 and 2020 were identified from a multi-institutional database. Futility was defined either as mortality or recurrence within 12 months of surgery. Various machine learning and deep learning techniques were used to develop prediction models for futile surgery. Results: Overall, 827 intrahepatic cholangiocarcinoma patients were included. Among 378 patients (45.7%) who had futile surgery, 297 patients (78.6%) developed intrahepatic cholangiocarcinoma recurrence and 81 patients (21.4%) died within 12 months of surgical resection. An ensemble model consisting of multilayer perceptron and gradient boosting classifiers that used 10 preoperative factors demonstrated the highest accuracy, with areas under receiver operating characteristic curves of 0.830 (95% confidence interval 0.798–0.861) and 0.781 (95% confidence interval 0.707–0.853) in the training and testing cohorts, respectively. The model displayed sensitivity and specificity of 64.5% and 80.0%, respectively, with positive and negative predictive values of 73.1% and 72.7%, respectively. Radiologic tumor burden score, serum carbohydrate antigen 19-9, and direct bilirubin levels were the factors most strongly predictive of futile surgery. The artificial intelligence–based model was made available online for ease of use and clinical applicability (https://altaf-pawlik-icc-futilityofsurgery-calculator.streamlit.app/). Conclusion: The artificial intelligence ensemble model demonstrated high accuracy to identify patients preoperatively at high risk of undergoing futile surgery for intrahepatic cholangiocarcinoma. Artificial intelligence–based prediction models can provide clinicians with reliable preoperative guidance and aid in avoiding futile surgical procedures that are unlikely to provide patients long-term benefits
