Ankit Kalucha
43 posts

Ankit Kalucha
@Ankitowledge
Easing oncology outcomes decision making
India Katılım Aralık 2023
457 Takip Edilen53 Takipçiler

⚡️ Final THOR-2 readout: oral erdafitinib improved RFS vs intravesical chemo in FGFR3/2-altered high-risk papillary NMIBC and showed durable CRs in CIS and intermediate-risk cohorts.
Safety in line with FGFR inhibitors.
Despite early stop/small N, it reinforces FGFR-targeted strategies in NMIBC and supports intravesical TAR-210.
#BladderCancer
sciencedirect.com/science/articl…


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Ankit Kalucha retweetledi

Does it mean ~100k oncologists globally ? That’s the number I used informally. @Ankitowledge

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Ankit Kalucha retweetledi

Do ΔORR and ΔDoR Predict PFS and OS Hazard Ratios Across Cancer RCTs? #CancerTrials #RCTs #MedicalAI

Suomi

CONSORT 2025 statement: updated guideline for reporting randomized trials | Nature Medicine nature.com/articles/s4159…
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@Ankitowledge I want to present the benefits of AI for cancer research to a support, lobby group and ultimately to researchers, trainee patient advocate.
Have you a synopsis?
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@brunolarvol @JoachimSchork @Jud_Prz @MedinDarko Distinct roles @brunolarvol PCA is for identifying correlation between important features from a given set
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@JoachimSchork @Ankitowledge @Jud_Prz @MedinDarko : is PCA old school (vs embedding) ? Or both have clear and distinct roles ? (Thanks @JoachimSchork for the post).
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Principal Component Analysis (PCA) is a powerful tool for dimensionality reduction, used to simplify complex data sets while retaining their most important features.
It transforms the original variables into a new set of uncorrelated variables called principal components. These components capture the maximum variance in the data, making it easier to analyze and visualize.
Key Points:
✔️ Simplifies data sets by reducing the number of variables.
✔️ Helps in identifying patterns and insights.
✔️ Improves the performance of machine learning models.
❌ May result in loss of some information.
❌ Interpretation of components can be challenging.
Practical Implementation:
🔹 R: Use prcomp() from the stats package for performing PCA.
🔹 Python: Use PCA from the sklearn.decomposition module for PCA implementation.
For a detailed step-by-step tutorial on PCA, including practical examples, check out my tutorials created in collaboration with Paula Villasante Soriano & @Cansu_SG.
Article: statisticsglobe.com/principal-comp…
Video: youtube.com/watch?v=DngS4L…
Furthermore, I have created an extensive introduction to PCA, which explains the theoretical concepts of PCA as well as how to apply it in R programming. More info: statisticsglobe.com/online-course-…
#database #DataScience #DataAnalytics #Rpackage #RStats #R #StatisticalAnalysis

YouTube

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Immunotherapy for advanced-stage squamous cell lung cancer
nature.com/articles/s4157…
@NatRevClinOncol
#LCSM

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@Ankitowledge @DrYukselUrun @ASCO @JCOCCI_ASCO @APCCC_Lugano @AarmstrongDuke @neerajaiims @yekeduz_emre @DrRanaMcKay @PBarataMD @TiansterZhang @PGrivasMDPhD @Silke_Gillessen @nataliagandur @OncBrothers @TwoOncDocs Let’s post our sentiment analysis ?
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KEYNOTE-921 Final Analysis: Adding pembrolizumab to docetaxel in mCRPC did not significantly improve rPFS.
@ASCO @JCOCCI_ASCO @APCCC_Lugano @AarmstrongDuke
ascopubs.org/doi/pdf/10.120…

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Deep research cost evaluation of the recent Stage IV NSCLC With Driver Alterations: ASCO Living Guideline v2024.3 #nsclc #ASCO #lungcancer #Oncology

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Comparison of 1st chemotherapy in unresectable locally advanced or metastatic #PDAC
@TheLancetOncol
doi.org/10.1016/S1470-…
🔎systematic review & Bayesian network meta-analysis, 79 trials, 22 168 pts
👉NALIRIFOX & FOLFIRINOX may be the preferred options if feasible
@myESMO

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