Ultrahigh Penetration and Retention of Graphene Quantum Dot Mesoporous Silica Nanohybrids for Image Guided Tumor Regression

Funding: This work was supported by Department of Biotechnology, Government of India. J.C. acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). We thank the staff of animal house, NCCS, Pune for supporting us during animal studies. We also thank Mr. Sumit for the discussion and Dr. Mukesh K. Kumawat for providing GQDs.So far, near-infrared (NIR) light responsive nanostructures have been well-defined in cancer nanomedicine. However, poor penetration and retention in tumors are the limiting factors. Here, we report the ultrahigh penetration and retention of carbanosilica (graphene quantum dots, GQDs embedded mesoporous silica) in solid tumors. After NIR light exposure, quick (0.5 h) emission from the tumor area is observed that is further retained up to a week (tested up to 10 days) with a single dose administration of nanohybrids. Emissive and photothermally active GQDs and porous silica shell (about 31% drug loading) make carbanosilica a promising nanotheranostic agent exhibiting 68.75% tumor shrinking compared to without NIR light exposure (34.48%). Generated heat (∼52 °C) alters the permeability of tumor enhancing the accumulation of nanotheranostics into the tumor environment. Successive tumor imaging ensures the prolonged follow-up of image guided tumor regression due to synergistic therapeutic effect of nanohybrids.publishersversionpublishe

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Phase 3 RCT comparing docetaxel-platinum with docetaxel-platinum-5FU as neoadjuvant chemotherapy in borderline resectable oral cancer

Background: Neoadjuvant chemotherapy (NACT) with TPF (docetaxel, cisplatin, and 5FU) is one of the treatment options in very locally advanced oral cancer with a survival advantage over PF (cisplatin and 5FU). TP (docetaxel and cisplatin) has shown promising results with a lower rate of adverse events but has never been compared to TPF. Methods: In this phase 3 randomized superiority study, adult patients with borderline resectable locally advanced oral cancers were randomized in a 1:1 fashion to either TP or TPF. After the administration of 2 cycles, patients were evaluated in a multidisciplinary clinic and further treatment was planned. The primary endpoint was overall survival (OS) and secondary endpoints were progression-free survival (PFS) and adverse events. Results: 495 patients were randomized in this study, 248 patients in TP arm and 247 in TPF arm. The 5-year OS was 18.5% (95% CI 13.8–23.7) and 23.9% (95% CI 18.1-30.1) in TP and TPF arms, respectively (Hazard ratio 0.778; 95% CI 0.637–0.952; P = 0.015). Following NACT, 43.8% were deemed resectable, but 34.5% underwent surgery. The 5-year OS was 50.7% (95% CI 41.5–59.1) and 5% (95%CI 2.9–8.1), respectively, in the surgically resected versus unresected cohort post NACT (P &lt; 0.0001). Grade 3 or above adverse events were seen in 97 (39.1%) and 179 (72.5%) patients in the TP and TPF arms, respectively (P &lt; 0.0001). Conclusion: NACT with TPF has a survival benefit over TP in borderline resectable oral cancers, with an increase in toxicity which is manageable. Patients who undergo surgery achieve a relatively good, sustained survival.</p