Randomized phase II study with two gemcitabine- and docetaxel-based combinations as first-line chemotherapy for metastatic non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Docetaxel and gemcitabine combinations have proven active for the treatment of non-small cell lung cancer (NSCLC). The aim of the present study was to evaluate and compare two treatment schedules, one based on our own preclinical data and the other selected from the literature.</p> <p>Methods</p> <p>Patients with stage IV NSCLC and at least one bidimensionally-measurable lesion were eligible. Adequate bone marrow reserve, normal hepatic and renal function, and an ECOG performance status of 0 to 2 were required. No prior chemotherapy was permitted. Patients were randomized to arm A (docetaxel 70 mg/m²on day 1 and gemcitabine 900 mg/m²on days 3–8, every 3 weeks) or B (gemcitabine 900 mg/m2 on days 1 and 8, and docetaxel 70 mg/m2 on day 8, every 3 weeks).</p> <p>Results</p> <p>The objective response rate was 20% (95% CI:10.0–35.9) and 18% (95% CI:8.6–33.9) in arms A and B, respectively. Disease control rates were very similar (54% in arm A and 53% in arm B). No differences were noted in median survival (32 vs. 33 weeks) or 1-year survival (33% vs. 35%). Toxicity was mild in both treatment arms.</p> <p>Conclusion</p> <p>Our results highlighted acceptable activity and survival outcomes for both experimental and empirical schedules as first-line treatment of NSCLC, suggesting the potential usefulness of drug sequencing based on preclinical models.</p> <p>Trial registration number</p> <p>IOR 162 02</p

Phase II study of gemcitabine, doxorubicin and paclitaxel (GAT) as first-line chemotherapy for metastatic breast cancer: a translational research experience

BACKGROUND: Patients with metastatic breast cancer are frequently treated with anthracyclines and taxanes, which are among the most active agents in this disease. Gemcitabine is an interesting candidate for a three-drug combination because of its different mechanism of action and non-overlapping toxicity with respect to the other two drugs. We aimed to evaluate the activity and toxicity of the GAT (gemcitabine, doxorubicin and paclitaxel) regimen, derived from experimental preclinical studies, as first-line chemotherapy in patients with stage IIIB-IV breast cancer. METHODS: Patients with locally advanced or metastatic breast cancer and at least one bidimensionally measurable lesion were included in the present study. Adequate bone marrow reserve, normal cardiac, hepatic and renal function, and an ECOG performance status of 0 to 2 were required. Only prior adjuvant non anthracycline-based chemotherapy was permitted. Treatment consisted of doxorubicin 50 mg/m(2 )on day 1, paclitaxel 160 mg/m(2 )on day 2 and gemcitabine 800 mg/m(2 )on day 6, repeated every 21–28 days. RESULTS: Thirty-three consecutive breast cancer patients were enrolled onto the trial (7 stage IIIB and 26 stage IV). All patients were evaluable for toxicity and 29 were assessable for response. A total of 169 cycles were administered, with a median of 6 cycles per patient (range 1–8 cycles). Complete and partial responses were observed in 6.9% and 48.3% of patients, respectively, for an overall response rate of 55.2%. A response was reported in all metastatic sites, with a median duration of 16.4 months. Median time to progression and overall survival were 10.2 and 36.4 months, respectively. The most important toxicity was hematological, with grade III-IV neutropenia observed in 69% of patients, sometimes requiring the use of granulocyte colony-stimulating factor (27%). Non hematological toxicity was rare and mild. One patient died from sepsis during the first treatment cycle before the administration of gemcitabine. CONCLUSION: The strong synergism among the three drugs found in the preclinical setting was confirmed in terms of both clinical activity and hematological toxicity. Our results seem to indicate that the GAT regimen is effective in anthracycline-naïve metastatic breast cancer and provides a feasible chemotherapeutic option in this clinical setting

Development of multimodal systems for monitoring paediatric brain disorders

In the last years, artificial intelligence (AI) methods are extensively applied in several fields, including healthcare, with several applications to support diagnostic approaches or treatments. The research activities carried on during my PhD work have been devoted to the development of AI methods to support neonatologists and paediatric neurologists in the detection, characterization, and monitoring of brain disorders in paediatric subjects. Specifically, the PhD work was focused on the development of multimodal systems for: neonatal and absence seizure detection; quantitative characterization of the speech phenotype for some genetic syndromes; prediction of the neurodevelopmental scales in newborns with sepsis. In the first part of this PhD work, absence seizure detectors have been developed both for online and offline applications based on Electroencephalographic (EEG) signals and sonification algorithms. Following the encouraging results obtained for absence seizures, first attempts were made to validate EEG-based Neonatal Seizure Detectors (NSDs), a still tricky and time-consuming issue in the clinical practice. Moreover, Heart rate variability (HRV) analysis was proposed as an alternative approach for the detection of neonatal seizures. Experimental results confirmed the involvement of the Autonomic Nervous System during or close to neonatal seizures. The comparison between EEG-based NSDs and HRV ones confirmed that the best approach to detect neonatal seizures is still the EEG. However, when EEG techniques are not available, the use of HRV-based NSDs could be a promising alternative. In the second part of this PhD work, quantitative acoustical analysis has been applied to the definition of the speech phenotype for four genetic syndromes: Down, Noonan, Costello and Smith-Magenis. Preliminary results confirm that acoustical measures could add helpful information for several syndromes with well-known language/voice impairments. Being completely non-invasive, acoustical analysis and AI methods might significantly contribute to the clinical assessment of such pathologies, also after surgical, pharmacological or logopaedic treatments and for long-term monitoring of the acoustical characteristics of the voice of these subjects. The last part of this PhD thesis exploits the possibility of forecasting neurodevelopmental scores in preterm newborns with and without sepsis. Using AI regression models, reliable results at different time steps of the follow-up were obtained, both with EEG and HRV features. The BAYLEY-III test was used to compute the scores in three different domains: cognitive, language and motor. Results suggest that both EEG and HRV quantitative analysis could be helpful for the clinical staff, identifying the newborns at risk of neurodevelopmental delays. Summing up, this PhD thesis shows how AI methods could be a valid support to clinicians in neurological paediatrics. Several experimental results are presented, showing possible applications and factual integration between AI techniques and clinical knowledge and needs, providing novel solutions and tools to support the clinical staff in the detection and characterization of brain diseases in infants and children

Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications

The human brain’s role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals’ cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200–300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90–140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones