python scripts/generate_schematic.py "your diagram description" -o figures/output.png

\maketitle
\thispagestyle{empty}

% Report Information Box
\begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information]
\textbf{Document Type:} Patient Cohort Analysis\\
\textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\
\textbf{Analysis Date:} \today\\
\textbf{Population:} 60 patients, biomarker-stratified by HR status
\end{tcolorbox}

\vspace{0.3cm}

% Key Finding #1: Primary Results
\begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results]
\begin{itemize}
    \item Overall ORR: 72\% (95\% CI: 59-83\%)
    \item Median PFS: 18.5 months (95\% CI: 14.2-22.8)
    \item Median OS: 35.2 months (95\% CI: 28.1-NR)
\end{itemize}
\end{tcolorbox}

\vspace{0.3cm}

% Key Finding #2: Biomarker Insights
\begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings]
\begin{itemize}
    \item HR+/HER2+: ORR 68\%, median PFS 16.2 months
    \item HR-/HER2+: ORR 78\%, median PFS 22.1 months
    \item HR status significantly associated with outcomes (p=0.041)
\end{itemize}
\end{tcolorbox}

\vspace{0.3cm}

% Key Finding #3: Clinical Implications
\begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations]
\begin{itemize}
    \item Strong efficacy observed regardless of HR status (Grade 1A)
    \item HR-/HER2+ patients showed numerically superior outcomes
    \item Treatment recommended for all HER2+ MBC patients
\end{itemize}
\end{tcolorbox}

\newpage
\tableofcontents  % TOC on page 2
\newpage  % Detailed content starts page 3

> Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression (<1%, 1-49%, ≥50%) 
> receiving pembrolizumab. Include outcomes: ORR, median PFS, median OS with hazard ratios 
> comparing PD-L1 ≥50% vs <50%. Generate Kaplan-Meier curves and waterfall plot.

> Generate cohort analysis for 30 GBM patients classified into Cluster 1 (Mesenchymal-Immune-Active) 
> and Cluster 2 (Proneural) molecular subtypes. Compare outcomes including median OS, 6-month PFS rate, 
> and response to TMZ+bevacizumab. Include biomarker profile table and statistical comparison.

> Analyze 60 HER2-positive metastatic breast cancer patients treated with trastuzumab-deruxtecan, 
> stratified by prior trastuzumab exposure (yes/no). Include ORR, DOR, median PFS with forest plot 
> showing subgroup analyses by hormone receptor status, brain metastases, and number of prior lines.

> Create evidence-based treatment recommendations for HER2-positive metastatic breast cancer including 
> biomarker-guided therapy selection. Use GRADE system to grade recommendations for first-line 
> (trastuzumab+pertuzumab+taxane), second-line (trastuzumab-deruxtecan), and third-line options. 
> Include decision algorithm flowchart based on brain metastases, hormone receptor status, and prior therapies.

> Generate treatment recommendation report for advanced NSCLC based on PD-L1 expression, EGFR mutation, 
> ALK rearrangement, and performance status. Include GRADE-graded recommendations for each molecular subtype, 
> TikZ flowchart for biomarker-directed therapy selection, and evidence tables from KEYNOTE-189, FLAURA, 
> and CheckMate-227 trials.

> Create treatment algorithm for newly diagnosed multiple myeloma through relapsed/refractory setting. 
> Include GRADE recommendations for transplant-eligible vs ineligible, high-risk cytogenetics considerations, 
> and sequencing of daratumumab, carfilzomib, and CAR-T therapy. Provide flowchart showing decision points 
> at each line of therapy.