\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Prognostic Modeling for Bone Sarcomas based on a Large Prospective
Cohort from a Tertiary Care Cancer Centre in India}



\author[1]{Laboni Sarkar}%
\author[1]{Jyoti Bajpai}%
\author[1]{Sushmita Rath}%
\author[1]{Akash Pawar}%
\author[1]{Arun Chandrasekharan}%
\author[1]{Goutam Panda}%
\author[1]{Dharmpal Jakar}%
\author[1]{Jaya Ghosh}%
\author[2]{Siddhartha Laskar}%
\author[1]{Bharat Rekhi}%
\author[2]{Nehal Khanna}%
\author[2]{Jifmi Manjali}%
\author[1]{Mukta Ramdawar}%
\author[1]{Nilendu Purandare}%
\author[1]{Prabhat Bhargava}%
\author[3]{Nivedita Chakrabarty}%
\author[3]{Kunal Gala}%
\author[1]{Yogesh Kembhavi}%
\author[1]{Venkatesh Rangarajan}%
\author[1]{Sripad Banavali}%
\author[1]{Sudeep Gupta}%
\affil[1]{Homi Bhabha National Institute}%
\affil[2]{Tata Memorial Centre Department of Radiation Oncology}%
\affil[3]{Tata Memorial Centre Department of Radiodiagnosis}%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
\textbf{Background:} Outcomes of adolescents and young adults (AYA) with
bone sarcomas inclusive of osteosarcoma (OGS) and Ewing's sarcoma (ES)
are impacted by various factors including inadvertent prior treatment
and poor compliance. We aimed to identify prognostic factors and derive
prognostic models for these patients. \textbf{Methods:} All AYA OGS and
ES cases treated at our institute with the ``OGS-12'' and Ewing's family
of tumors-2001 (``EFT-2001'') protocols from 2011 to 2021, and 2013 to
2018 respectively, were prospectively analyzed. \textbf{Results:}. Among
606/748 (81.0\%) AYA with non-metastatic osteosarcoma, significant
factors included in the prognostic model were failure to complete
protocol (hazard ratio (HR) 2.65, 95\% confidence interval (CI)
1.65-4.26), prior treatment (HR 2.93, CI 1.4-6.1), necrosis
\textless{}90\% (HR 1.63, CI 1.24-2.1), joint involvement (HR 2.0, CI
1.49-2.69) and SAP\textgreater{} median (204 U/l) (HR 1.63, CI
1.24-2.14). Of 104/263 (39.5\%) AYA ES, significant factors were failure
to complete protocol (HR 2.84, CI 1.03-7.8), prior treatment (HR 6.37,
CI 1.8-22.0), necrosis \textless{}100\% (HR 8.73, CI 2.16-35.3), and
tumor size \textgreater{}8cm (HR 2.64, CI 1.04-6.7). For 142/366
(38.8\%) AYA with metastatic OGS, significant factors were failure to
complete protocol (HR 5.29), metastases not amenable to local treatment
(HR 1.96), necrosis \textless{}90\% (HR 1.96), and \textgreater{}10
metastases (HR 2.44). For 38/82 (43.6\%) AYA with metastatic extremity
ES, significant factors were failure to complete protocol (HR 3.88) and
metastases not amenable to local treatment (HR 10.6).
\textbf{Conclusion:} We developed simple, effective prognostic models
for AYA with bone sarcomas with wide applicability in LMIC.%
\end{abstract}%



\sloppy


\textbf{Introduction:}

Bone sarcomas inclusive of osteosarcoma (OGS) and Ewing's sarcoma (ES)
are rare, aggressive yet chemosensitive tumors with variable survival
rates in Low and Middle-Income Countries (LMIC). There are various
factors including delayed presentation with advanced disease and higher
rates of metastatic disease, inadvertent prior treatment by primary care
physicians, high dropout rates and poor adherence due to socioeconomic
constraints with resultant compromised outcomes.
Non-High-Dose-Methotrexate (Non-HDMTX) based regimens for osteosarcoma
have gained popularity in these regions due to ease of administration on
daycare basis, low cost and convenience. Therefore, finding valuable
prognostic factors specifically targeting LMIC is crucial for predicting
high-risk patients and early intervention to improve survival rates.

Among patients with osteosarcoma, possible prognostic factors, tumor
size, metastatic disease at the time of diagnosis, histological grade,
histologic response to neoadjuvant chemotherapy (NACT), and adequate
surgical margins have consistently shown a strong correlation with
survival. {[}1-2{]} Non-uniformity of chemotherapy protocols for
osteosarcoma preclude generalisability of results. We have previously
published our experience with our institutional standard low cost,
non-HDMTX based ``OGS-12'' protocol where serum alkaline phosphatase
level for EFS and performance status for OS were found to be independent
prognosticators, concordant with other reports, including those from
India {[}3-6{]}. Histological response to NACT was an independent
predictor of both EFS and OS which is also well-established.

For Ewing's sarcoma, multiple prognostic factors have been reported,
such as age, gender, localization, volume and size of the primary tumor,
presence of metastasis, treatment regimens, a baseline level of
hemoglobin or lactate dehydrogenase, and pathologic response to
neoadjuvant chemotherapy {[}7-9{]}. In our published data on patients
with Ewing's sarcoma at our institute treated with the ``EFT-2001''
protocol, statistically significant prognostic factors included longer
symptom duration, \textgreater{}= 99\% necrosis, and protocol
completion. {[}10{]}

There are unique challenges with rare cancers like bone sarcomas
globally and a prognostic model with wide applicability including for
the patients in LMIC wherein including social challenges also play a
role is an unmet need. Special challenges in LMIC include lack of
awareness among patients and primary health care providers with
resultant delayed presentation, upstaging and high tumor burden.

We have aimed to identify prognostic factors including those specific to
LMIC. Additionally, we have derived and validated a prognostic score for
osteosarcoma that integrates biologic and social factors and is tailored
for patients from an LMIC setting using a non-HDMTX-based protocol.

\textbf{Methods:}

All adolescent and adult OGS and ES cases treated at Tata Memorial
Centre, Mumbai, with the ``OGS-12'' and Ewing's family of tumors-2001
(``EFT-2001'') protocol from November 2011 to January 2021, and January
2013 to November 2018 respectively were prospectively analyzed
separately. The ``OGS-12'' protocol consists of doublets of doxorubicin,
ifosfamide and cisplatin given sequentially for eight cycles in both
neoadjuvant (NACT) and adjuvant (ACT) settings. The ``EFT-2001''
protocol consists of a 12-month course of ifosfamide plus etoposide and
vincristine, doxorubicin plus cyclophosphamide. Demographic factors
recorded were age, gender and nutritional parameters. Disease factors
were symptom duration, lactate dehydrogenase (LDH), serum alkaline
phosphatase (SAP), tumor size, presence and sites of metastases,
pathological fracture and neurovascular bundle involvement. Patients
were stratified based on age into the following categories: 15-25 and
\textgreater{}25-39 to evaluate prognostic significance of age. Patients
with metastatic disease were stratified into categories based on number
(\textless{}=10, \textgreater{}10), location (lung, bone, or other) and
whether metastases were amenable to local treatment based on
multimodality joint clinic discussion. Toxicities were documented using
the US National Cancer Institute-Common Toxicity Criteria for Adverse
Events (NCI-CTCAE) version 4.0. Histological response on the surgical
specimen was assessed using Huvo's necrosis grading, wherein good
responders were defined as those with \textless{}10\% viable cells
{[}11{]}.

Treatment characteristics recorded recorded from electronic medical
records (EMR) included markers of non-compliance (failure to complete
protocol and failure to complete treatment within the planned period
with 25\% additional time to allow for justifiable reasons for
non-compliance), prior inadvertent treatment by peripheral
practitioners, and febrile neutropenia as a marker of chemosensitivity.

The study was conducted in accordance with ethical principles outlined
in the Declaration of Helsinki (Fortaleza, Brazil 2013), ICH-GCP,
European Directive 2001/20/EC, US Code of Federal Regulations, South
African Good Clinical Practice Guidelines, and other institutional
regulatory requirements. Data collection and analyses were conducted in
a single institution. This study is registered with Clinical Trials
Registry---India (CTRI) identifier: CTRI/2023/10/059247.

\textbf{Statistical Analysis:}

Data was analysed using IBM SPSS version 25 Inc, Chicago, IL and RStudio
software version 2023.03.0. Descriptive statistics were represented as
median or percentages. T-tests or linear regression models were used to
analyse continuous measures and chi-square tests, tests of proportions
and logistic regressions were used to analyse binary data. Survival was
estimated using Kaplan-Meier method and compared using a log-rank test.
The primary outcome was event-free survival (EFS). Secondary outcomes
were overall survival (OS) and histological necrosis. Effect of
covariates on survival were estimated using Cox proportional hazards
analysis. Significant factors on univariate analysis were tested in
multivariate analysis.

\textbf{Generation of the derivation and validation cohorts and
identification of prognostic factors in the derivation cohort}

The whole cohort was divided in a 2:1 ratio into a derivation and
validation cohort in a randomized fashion. Univariable Cox regression
analyses identified factors prognostic for EFS in the derivation cohort.
Factors with p\textless{}0.05 on univariable analyses were included for
multivariable analysis in a backward stepwise fashion based on
likelihood ratio. Factors with p\textless{}0.05 in the final
multivariable model in the derivation cohort were used to formulate the
risk score.

\textbf{Formulation of risk score}

A weighted score was provided to each prognostic variable. The score was
computed based on the approximate ratios of the beta coefficients of
each factor in the multivariable model. The total score was calculated
by summation of individual prognostic factor scores and was used to
divide patients into three clinically discriminatory risk groups.

\textbf{Validation of the risk score}

The risk score was validated by applying it separately to the
derivation, validation, and whole cohorts separately. Kaplan Meier
curves were constructed to represent EFS and OS in the three risk groups
in each of the three cohorts. Harrell's concordance index (c-index) was
calculated for estimating the predictive ability of the risk category
model for EFS and OS in the derivation, validation and whole cohorts. A
receiver operating characteristic (ROC) curve was also constructed by
comparing the predicted and actual 18-month and 36-month EFS and OS in
each of the three cohorts and the timed area under the ROC curve (timed
AUC) for the derivation, validation and whole cohort was estimated.

\textbf{Results:}

\textbf{Baseline Characteristics:}

There were 1103 AYA with OGS registered during the study period, of whom
748 (67.8\%) were extremity osteosarcomas uniformly treated with the
``OGS-12'' protocol as first-line therapy, consisting of NACT, surgery
and ACT. (Figure 1) Of these, 606 (81.0\%) had non-metastatic disease.
There were 263 non-metastatic ES, of whom 104 (39.5\%) had extremity
tumours treated with the ``EFT-2001'' protocol. Among 218 AYA with
metastatic ES, 82 (37.6\%) received the ``EFT-2001'' protocol, of whom
(46.3\%) had extremity tumours while the rest (chest wall, pelvis or
other non-appendicular primary) were not included in this analysis as
prespecified. Baseline characteristics are detailed in Table 1 and Table
2.

\textbf{Treatment Compliance, Protocol Completion and Prior Inadvertent
Treatment:}

561 (92.6\%) non-metastatic OGS were treatment-na\selectlanguage{ngerman}ïve at presentation, of
whom 481 (85.7\%) completed the entire treatment protocol consisting of
NACT followed by surgery and ACT. Median total treatment duration was 27
(21-85) weeks. Of metastatic OGS, 83 (58.4\%) patients completed the
entire treatment protocol inclusive of NACT, Surgery and ACT, while 15
(10.5\%) had received prior treatment.

Among non-metastatic ES, 91 (87.5\%) were treatment-naïve at
presentation, 79 (75.9\%) underwent surgery after NACT, and 70 (67.3\%)
completed the treatment protocol.

\textbf{Histological Necrosis:}

Of 481 non-metastatic and 111 metastatic OGS patients analysable for
histological response, there were 284 (59.0\%) and 55 (49.5\%) good
responders respectivelypathological complete response (100\% necrosis).
For metastatic ES, 10 (45.5\%) patients had good histological response
with 5 (22.7\%) attaining 100\% necrosis.

\textbf{Compliance:}

Of 1566 registered AYA with bone sarcomas, 182 (11.6\%) defaulted after
diagnosis. among the treatment naive cohorts enrolled respectively on
the ``OGS-12'' and ``EFT-2001'' protocols, 42 (6.1\%) osteosarcoma and
18 (14.2\%) Ewing's sarcoma abandoned treatment subsequently. Overall,
379/481 (78.8\%) non-metastatic OGS and 51/70 (72.8\%) non-metastatic ES
treated per-protocol were compliant to treatment.

\textbf{Chemotoxicity:}

264 (43.5\%) non-metastatic OGS, 53 (37.3\%) metastatic OGS, 54 (51.9\%)
non-metastatic ES and 16 (42.1\%) metastatic ES cases had febrile
neutropenia, with 121 (19.9\%) non-metastatic OGS, 48 (33.8\%)
metastatic OGS, 24 (23.1\%) non-metastatic ES and 14 (36.8\%) metastatic
ES requiring dose modification of at least one drug, respectively.

\textbf{Outcomes:}

For non-metastatic OGS, at a median follow-up of 59.7 (95\% confidence
interval (CI): 54.4-63.3) months, 5-year event free survival (5-EFS) was
54.8 (CI: 50.5-59.4)\%, and 5-year overall survival (5-OS) was 72.7 (CI:
68.7-77.0)\%. For metastatic OGS, 5-EFS was 18.7 (CI: 12.7-27.5)\%. For
non-metastatic and metastatic ES, 5-EFS were 68.8 (CI: 59.5-79.5)\% and
21.9\% (CI 11.4-42.0)\%.

\textbf{Prognostic Factors}

\textbf{OGS:}

For non-metastatic OGS, factors found significant on univariate analysis
are shown in Table 3. On multivariable analysis, higher baseline SAP,
neurovascular bundle involvement, joint involvement, poor histological
necrosis, failure to complete treatment, prior treatment independently
predicted inferior EFS.

In the metastatic cohort, failure to complete treatment protocol, poor
histological necrosis, tumours not amenable to local treatment and
\textgreater{}10 metastases were also predictive of superior EFS on
multivariable analysis. (Table 4)

\textbf{ES:}

On multivariable analysis, failure to complete treatment protocol, prior
treatment, residual viable tumor and tumor size \textgreater{}8cm were
independent predictors for inferior EFS for non-metastatic tumours.
(Table 5)

In the metastatic cohort, failure to complete protocol and tumours not
amenable to local treatment independently predicted inferior EFS. (Table
6)

\textbf{Model:}

\textbf{Non-Metastatic Osteosarcoma:}

Variables included in the model for the whole cohort included failure to
complete protocol (8.7 points), prior treatment (10.0 points), necrosis
\textless{}90\%: 4.3 points, neurovascular bundle involvement: 4.4
points, joint involvement: 6.3 points and SAP\textgreater{} median (204
U/l): 5.2 points (Table 7). In the validation cohort (n=158), factors
that emerged significant and included in the final model were failure to
complete protocol (7.1 points), prior treatment (10.0 points), necrosis
\textless{}90\%: 2.9 points, joint involvement: 5.1 points and
SAP\textgreater{} median (204 U/l): 3.0 points (Table 8). Stratification
was done by summation of the individual scores as follows; low risk
(score \textless{}3), intermediate risk (3 - 8) and high risk
(\textgreater{}8). Kaplan-Meier curves for EFS for the derivation
(n=342), validation (n=158) and whole (n=600) cohort are depicted in
Figure 2A{[}i{]}, 2A{[}ii{]} and 2A{[}iii{]}. The c-index for whole,
derivation and validation data are were 0.659, 0.664 and 0.651
respectively.

\textbf{Non-Metastatic Ewing's Sarcoma:}

Factors significant in the cohort and included the final model were
failure to complete protocol (4.8 points), prior treatment (8.5 points),
necrosis \textless{}100\% (10.0 points), and tumor size
\textgreater{}8cm (4.5 points), depicted in the nomogram. (Figure 2B).

\textbf{Metastatic Osteosarcoma}

Factors significant in the cohort and included the final model were
failure to complete protocol (10.0 points), in-amenability to local
treatment (1.8 points), necrosis \textless{}90\% (4.1 points), and
\textgreater{}10 metastases (5.4 points), depicted in the nomogram
(Figure 2C).

\textbf{Metastatic Ewing's Sarcoma}

Factors significant in the cohort and included the final model were
failure to complete protocol (5.7 points) and not being amenable to
local treatment (10 points), depicted in the nomogram. (Figure 2D)

\textbf{Discussion:}

In this study, we analyzed a retrospective cohort of AYA with
osteosarcoma treated at our center using a uniform non-HDMTX-based
protocol ``OGS-12'', and Ewing's sarcoma treated with our institutional
standard ``EFT-2001'' protocol. We formulated and validated prognostic
scores separately for metastatic and non-metastatic osteosarcoma and
Ewing's sarcoma based on baseline clinical factors and tailored to a
unique population of patients treated in a resource constrained setting
with our in-house, low-cost, institutional standard protocols which have
previously demonstrated survival outcomes are comparable to those
published from Western countries {[}12-15{]}.

For non-metastatic AYA sarcomas, features of high tumor burden including
higher baseline SAP, neurovascular bundle involvement and joint
involvement predicted poor EFS. This is consistent with published
literature - serum alkaline phosphatase and lactate dehydrogenase are
surrogates of osteoblastic activity and thus, elevated levels may
indicate increased disease aggressiveness {[}16{]}. Additionally,
vascular involvement noted radiologically in pretreatment MRI has been
found independently to be an risk factor for OS and EFS in patients with
Enneking IIB primary osteosarcoma involving extremities. {[}17{]} Poor
histological necrosis also predicted inferior EFS, which is well
described in existing literature.

Importantly, failure to complete treatment and prior treatment
independently predicted inferior EFS. This is particularly significant
in LMIC, with high treatment abandonment rates as well as failure to
complete treatment protocol due to various issues including financial
challenges, lack of education and motivation for treatment and logistic
issues relating to the need to travel long distances and stay far from
the patient's hometown for the duration of treatment due to
unavailability of cancer care near the place of residences. Thus, prompt
referral to dedicated centres specializing in cancer care and awareness
of primary health care physicians to recognize bone sarcomas and avoid
inadvertent inappropriate medical or surgical intervention is of
paramount importance.

For Ewing's sarcoma, tumor size \textgreater{}8cm was an additional
prognostic factor, consistent with reported literature. {[}18-19{]} In
the metastatic cohort, failure to complete treatment protocol, poor
histological necrosis, tumours not amenable to local treatment and
\textgreater{}10 metastases were also predictive of superior EFS on
multivariable analysis. determinants of survival. This underscores the
importance of multimodality clinics to identify tumors amenable to
curative treatment and avoid overtreatment for patients with extensive
disease. It has been previously observed that osteosarcoma presenting
only with lung metastases have better survival outcomes than metastases
at other sites {[}20{]}.

The factors that emerged prognostically significant for our patients
were similar to those described in data from centres using HDMTX-based
protocols, indicating the wide applicability of our results. For
non-HDMTX regimens, particularly prevalent in LMICs, data on prognostic
factors are scarce with only few observational studies {[}21-23{]}.
Histologic response to chemotherapy has been uniformly reported to be a
predictive factor in these studies. A study from Brazil has additionally
reported presence of metastases at baseline, primary tumor site and type
of surgery (amputation vs. limb sparing) as prognostically significant
{[}24{]}. Nevertheless, the lacunae for studies specifically targeting
bone sarcomas in LMICs remains apparent. Smaller sample sizes of
existing studies, observational nature, and non-uniformity of treatment
protocols makes generalizability of these results challenging. A
potential strategy to overcome these shortcomings include collaborative
efforts with multi-institution studies to enhance understanding of
osteosarcoma in LMICs. Importantly, several existing studies have
included patients with both non-metastatic and metastatic tumors, with
widely differing outcomes. We have addressed this limitation by
separately analyzed homogenously treated populations of metastatic and
non-metastatic osteosarcoma and Ewing's sarcoma.

In our study, we developed separate prognostic models for non-metastatic
and metastatic osteosarcoma and Ewing's sarcoma based on the prognostic
factors identified. We validated the risk score for non-metastatic
osteosarcoma, and demonstrated effective discriminative ability for
event-free survival between the three risk groups. For the other cohorts
(metastatic osteosarcoma, and non-metastatic and metastatic Ewing's
sarcoma), we developed prognostic models depicted as nomograms, although
validation could not be separately performed due to the limited sample
size. Available prognostic models for risk stratification in
osteosarcoma involve variable treatment modalities, clinician
preferences, surgical expertise and chemotherapy protocols which
precludes generalizability of these models. {[}25-28{]}. For Ewing's
sarcoma, existing models have largely utilized treatment-related factors
such choice of local treatment, factors that vary in different centres
due to individual clinician preference and expertise. {[}29-34{]} In
developing our prognostic models, we have analyzed homogenously treated
cohorts and reinforced the importance of baseline indicators of tumor
aggressiveness, chemosensitivity as indicated by histologic response to
chemotherapy, and general treatment-related factors unique to patients
in LMIC settings, such as failure to complete treatment protocol and
prior inadvertent treatment before referral to advanced oncology
centres.

The prognostic models we report are important and unique as they are
derived from a prospective analysis of homogenously treated AYA in a
LMIC setting. The specific challenges faced by this vulnerable subset of
patients has been stressed upon. The wide implications of our results
include healthcare policy-making, in the context of need for early
identification by appropriate awareness and training of primary care
physicians and timely referral to sarcoma reference centres, and the
importance of strategies to improve protocol completion by efforts such
as targeted nutritional intervention, extended growth factor support and
patient navigation facilities. Risk stratification of patients aids
selection of patients who would benefit from aggressive treatment as
well as those who may achieve comparable outcomes with less aggressive
modalities with improved quality of life. Traditionally, escalation of
treatment for patients with high-risk disease has been based on
histologic response after completion of neoadjuvant chemotherapy
{[}35-36{]}. Use of other important prognostic factors as described in
our study may be used to identify patients who may benefit from a less
intensive approach such as metronomic chemotherapy regimens and early
incorporation of palliative care, particularly in resource-constrained
settings {[}37-38{]}.

The prognostic implication of common social challenges have been
highlighted, including prior inadvertent treatment and the importance of
compliance in the form of protocol completion. We did not analyze
socioeconomic strata due to unavailability of uniform data, however,
surrogate parameters including compliance as assessed by treatment
duration and protocol completion, as well as prior inadvertent treatment
were analyzed. Importantly, majority of our patients are constrained
financially and socially and depend on financial aid from various
government schemes for cancer treatment. Another study from India has
evaluated location of primary residence of the patient and the distance
of the residence from the hospital and not found prognostic significance
for osteosarcoma {[}23{]}.

\textbf{Strengths and Limitations:}

This is the largest, single-centre prospective analysis of prognostic
factors for AYA with bone sarcomas globally, with the first prognostic
model tailored for AYA with generalizable results for LMIC.

Limitations include the shorter follow-up and lower number of AYA with
Ewing's sarcoma, which is as expected for a rare cancer. Additionally,
the prognostic scores for metastatic osteosarcoma, and for both
non-metastatic and metastatic osteosarcoma could not be validated in a
separation validation cohort due to lower sample sizes. Socioeconomic
backgrounds of our patients were not assessed with objective scales such
as the Kuppuswamy scale; however, surrogate parameters such as treatment
duration as an indicator for compliance, prior inadvertent treatment and
protocol completion were included in the analysis.

\textbf{Conclusion:}

We conclude that simple prognostic models predict the prognosis in
individual homogenous cohorts of adolescent and young adult patients
with non-metastatic and metastatic osteosarcoma and Ewing's sarcomas. In
addition to conventional prognosticators, failure to complete the
treatment protocol (all cohorts) and the disease not being amenable to
curative treatment (metastatic cohort) were associated with inferior
outcomes. These simple prognostic models are effective and with wide
applicability including in LMIC and merit appropriate recognition.

\textbf{Acknowledgements}

We thank Ms Priyanka Patil, Ms Sushama Gavit and our team of trial
coordinators for their valuable efforts in telephonically following up
our patients, and the entire Bone and Soft Tissues (BST) disease
management group (DMG) and the Department of Medical Oncology, Tata
Memorial Hospital, Mumbai, for all their inputs during clinical
decision-making in various cases. We would also like to express our
gratitude towards our patients and their caregivers.

\textbf{References}

\begin{enumerate}
\tightlist
\item
  Eleutério SJ, Senerchia AA, Almeida MT, et al. Osteosarcoma in
  patients younger than 12 years old without metastases have similar
  prognosis as adolescent and young adults. Pediatr Blood Cancer.
  2015;62(7):1209-1213. doi:10.1002/pbc.25459
\item
  Clark JC, Dass CR, Choong PF. A review of clinical and molecular
  prognostic factors in osteosarcoma. J Cancer Res Clin Oncol.
  2008;134(3):281-297. doi:10.1007/s00432-007-0330-x
\item
  Bajpai J, Chandrasekharan A, Talreja V, et al. Outcomes in
  non-metastatic treatment naive extremity osteosarcoma patients treated
  with a novel non-high dosemethotrexate-based, dose-dense combination
  chemotherapy regimen 'OGS-12'. Eur J Cancer. 2017;85:49-58.
  doi:10.1016/j.ejca.2017.08.013
\item
  Thorpe WP, Reilly JJ, Rosenberg SA. Prognostic significance of
  alkaline phosphatase measurements in patients with osteogenic sarcoma
  receiving chemotherapy. Cancer. 1979;43(6):2178-2181.
  doi:10.1002/1097-0142(197906)43:6\textless{}2178::aid-cncr2820430603\textgreater{}3.0.co;2-9
\item
  Li X, Ashana AO, Moretti VM, Lackman RD. The relation of tumour
  necrosis and survival in patients with osteosarcoma. Int Orthop.
  2011;35(12):1847-1853. doi:10.1007/s00264-011-1209-7
\item
  Bacci G, Mercuri M, Longhi A, et al. Grade of chemotherapy-induced
  necrosis as a predictor of local and systemic control in 881 patients
  with non-metastatic osteosarcoma of the extremities treated with
  neoadjuvant chemotherapy in a single institution. Eur J Cancer.
  2005;41(14):2079-2085. doi:10.1016/j.ejca.2005.03.036
\item
  Cotterill SJ, Ahrens S, Paulussen M, et al. Prognostic factors in
  Ewing's tumor of bone: analysis of 975 patients from the European
  Intergroup Cooperative Ewing's Sarcoma Study Group. J Clin Oncol.
  2000;18(17):3108-3114. doi:10.1200/JCO.2000.18.17.3108
\item
  Balamuth NJ, Womer RB. Ewing's sarcoma. Lancet Oncol.
  2010;11(2):184-192. doi:10.1016/S1470-2045(09)70286-4
\item
  Duchman KR, Gao Y, Miller BJ. Prognostic factors for survival in
  patients with Ewing's sarcoma using the surveillance, epidemiology,
  and end results (SEER) program database. Cancer Epidemiol.
  2015;39(2):189-195. doi:10.1016/j.canep.2014.12.012
\item
  Bajpai J, Panda GS, Chandrasekharan A, et al. Adolescent-adult
  nonmetastatic Ewing sarcoma-Experience from a large developing
  country. Pediatr Blood Cancer. 2021;68(9):e29081.
  doi:10.1002/pbc.29081
\item
  Wunder JS, Paulian G, Huvos AG, et al. The histological response to
  chemotherapy as a predictor of the oncological outcome of operative
  treatment of Ewing sarcoma.~J Bone Joint Surg Am.
  1998;80(7):1020-1033. doi:10.2106/00004623-199807000-00011
\item
  Meyers PA, Heller G, Healey J, et al. Chemotherapy for nonmetastatic
  osteogenic sarcoma: the Memorial Sloan-Kettering experience. J Clin
  Oncol. 1992;10(1):5-15. doi:10.1200/JCO.1992.10.1.5
\item
  Schwartz CL, Gorlick R, Teot L, et al. Multiple drug resistance in
  osteogenic sarcoma: INT0133 from the Children's Oncology Group. J Clin
  Oncol. 2007;25(15):2057-2062. doi:10.1200/JCO.2006.07.7776
\item
  Bielack S, Kempf-Bielack B, Schwenzer D, et al. Neoadjuvante Therapie
  des lokalisierten Osteosarkoms der Extremitäten. Erfahrungen der
  Cooperativen Osteosarkomstudiengruppe COSS an 925 Patienten
  {[}Neoadjuvant therapy for localized osteosarcoma of extremities.
  Results from the Cooperative osteosarcoma study group COSS of 925
  patients{]}. Klin Padiatr. 1999;211(4):260-270.
  doi:10.1055/s-2008-1043798
\item
  Meyer WH, Pratt CB, Poquette CA, et al. Carboplatin/ifosfamide window
  therapy for osteosarcoma: results of the St Jude Children's Research
  Hospital OS-91 trial. J Clin Oncol. 2001;19(1):171-182.
  doi:10.1200/JCO.2001.19.1.171
\item
  Thorpe WP, Reilly JJ, Rosenberg SA. Prognostic significance of
  alkaline phosphatase measurements in patients with osteogenic sarcoma
  receiving chemotherapy. Cancer. 1979;43(6):2178-2181.
  doi:10.1002/1097-0142(197906)43:6\textless{}2178::aid-cncr2820430603\textgreater{}3.0.co;2-9
\item
  Jin Q, Xie X, Yao H, et al. Clinical Significance of the Radiological
  Relationship between the Tumor and the main blood vessels in Enneking
  IIB Osteosarcoma of the extremities. \emph{J Cancer} .
  2020;11(11):3235-3245. Published 2020 Mar 5. doi:10.7150/jca.42341
\item
  Rodríguez-Galindo C, Liu T, Krasin MJ, et al. Analysis of prognostic
  factors in ewing sarcoma family of tumors: review of St. Jude
  Children's Research Hospital studies. Cancer. 2007;110(2):375-384.
  doi:10.1002/cncr.22821
\item
  Stahl M, Ranft A, Paulussen M, et al. Risk of recurrence and survival
  after relapse in patients with Ewing sarcoma. Pediatr Blood Cancer.
  2011;57(4):549-553. doi:10.1002/pbc.23040
\item
  Longhi A, Fabbri N, Donati D, et al. Neoadjuvant chemotherapy for
  patients with synchronous multifocal osteosarcoma: results in eleven
  cases. J Chemother. 2001;13(3):324-330. doi:10.1179/joc.2001.13.3.324
\item
  Sukumaran RK, Rajeshwari B, Sugath S, Chellappan SG, Thankamony P,
  Parukuttyamma K. Methotrexate Free Chemotherapy and Limb Salvage
  Surgery for Paediatric Osteosarcoma in India. Indian J Orthop.
  2018;52(1):58-64. doi:10.4103/ortho.IJOrtho\_195\_17
\item
  Dharanikota A, Arjunan R, Dasappa A. Factors Affecting Prognosis and
  Survival in Extremity Osteosarcoma. Indian J Surg Oncol.
  2021;12(1):199-206. doi:10.1007/s13193-020-01277-2
\item
  Ganguly S, Sasi A, Khan SA, et al. Formulation and validation of a
  baseline prognostic score for osteosarcoma treated uniformly with a
  non-high dose methotrexate-based protocol from a low middle income
  healthcare setting: a single centre analysis of 594 patients. Front
  Oncol. 2023;13:1148480. Published 2023 Apr 28.
  doi:10.3389/fonc.2023.1148480
\item
  Petrilli AS, Brunetto AL, Cypriano Mdos S, et al. Fifteen Years'
  Experience of the Brazilian Osteosarcoma Treatment Group (BOTG): A
  Contribution from an Emerging Country. J Adolesc Young Adult Oncol.
  2013;2(4):145-152. doi:10.1089/jayao.2013.0012
\item
  Song K, Song J, Chen F, Lin K, Ma X, Jiang J. Prognostic nomograms for
  predicting overall and cancer-specific survival of high-grade
  osteosarcoma patients. J Bone Oncol. 2018;13:106-113. Published 2018
  Oct 4. doi:10.1016/j.jbo.2018.09.012
\item
  Lu S, Wang Y, Liu G, et al. Construction and validation of nomogram to
  predict distant metastasis in osteosarcoma: a retrospective study. J
  Orthop Surg Res. 2021;16(1):231. Published 2021 Mar 30.
  doi:10.1186/s13018-021-02376-8
\item
  Yang QK, Lai QY, Wang Y, Wang Y, Yao ZX, Zhang XJ. Establishment and
  validation of prognostic nomograms to predict overall survival and
  cancer-specific survival for patients with osteosarcoma. Neoplasma.
  2021;68(2):434-446. doi:10.4149/neo\_2020\_200617N639
\item
  Zhang J, Yang J, Wang HQ, et al. Development and validation of a
  nomogram for osteosarcoma-specific survival: A population-based
  study.~\emph{Medicine (Baltimore)} . 2019;98(23):e15988.
  doi:10.1097/MD.0000000000015988
\item
  Gao F, Zhou Y, Zhao R, Ren Y. Establishing a novel prognostic tool for
  Ewing sarcoma patients: Surveillance, Epidemiology, and End Results
  database analysis. Medicine (Baltimore). 2020;99(46):e23050.
  doi:10.1097/MD.0000000000023050
\item
  Zhang J, Pan Z, Yang J, Yan X, Li Y, Lyu J. A nomogram for determining
  the disease-specific survival in Ewing sarcoma: a population study.
  BMC Cancer. 2019;19(1):667. Published 2019 Jul 5.
  doi:10.1186/s12885-019-5893-9
\item
  Zhou Q, Wu ZY, Lin ZQ. A nomogram to predict prognosis in Ewing
  sarcoma of bone. J Bone Oncol. 2019;15:100223. Published 2019 Feb 12.
  doi:10.1016/j.jbo.2019.100223
\item
  Bosma SE, Lancia C, Rueten-Budde AJ, et al. Easy-to-use clinical tool
  for survival estimation in Ewing sarcoma at diagnosis and after
  surgery. Sci Rep. 2019;9(1):11000. Published 2019 Jul 29.
  doi:10.1038/s41598-019-46721-8
\item
  Zhou Z, Wang J, Fang L, Ma J, Guo M. A nomogram for predicting overall
  survival in patients with Ewing sarcoma: a SEER-based study. BMC
  Musculoskelet Disord. 2020;21(1):737. Published 2020 Nov 12.
  doi:10.1186/s12891-020-03706-3
\item
  Shi J, Yang J, Ma X, Wang X. Risk factors for metastasis and poor
  prognosis of Ewing sarcoma: a population based study. J Orthop Surg
  Res. 2020;15(1):88. Published 2020 Mar 4.
  doi:10.1186/s13018-020-01607-8
\item
  Nataraj V, Batra A, Rastogi S, et al. Developing a prognostic model
  for patients with localized osteosarcoma treated with uniform
  chemotherapy protocol without high dose methotrexate: A single-center
  experience of 237 patients. J Surg Oncol. 2015;112(6):662-668.
  doi:10.1002/jso.24045
\item
  Wagner MJ, Livingston JA, Patel SR, Benjamin RS. Chemotherapy for Bone
  Sarcoma in Adults. J Oncol Pract. 2016;12(3):208-216.
  doi:10.1200/JOP.2015.009944
\item
  Bajpai J, Chandrasekharan A, Simha V, et al. Outcomes in
  Treatment-Naïve Patients With Metastatic Extremity Osteosarcoma
  Treated With OGS-12, a Novel Non-High-Dose Methotrexate-Based,
  Dose-Dense Combination Chemotherapy, in a Tertiary Care Cancer Center.
  J Glob Oncol. 2018;4:1-10. doi:10.1200/JGO.17.00137
\item
  Mailankody S, Kumar VS, Khan SA, Banavali SD, Bajpai J.
  Resource-appropriate selection of osteosarcoma treatment protocols in
  low- and middle-income countries. Pediatr Blood Cancer.
  2022;69(3):e29540. doi:10.1002/pbc.29540
\end{enumerate}

LEGENDS:

Figure 1: Figure 1 is a CONSORT diagram depicting the number of patients
analyzed in the current study

Figure 2A{[}i{]}: Figure 2A{[}i{]} is a Kaplan-Meier curve depicting
event-free survival for the patients with non-metastatic osteosarcoma:
derivation cohort (n=342)

Figure 2A{[}ii{]}: Figure 2A{[}ii{]} is a Kaplan-Meier curve depicting
event-free survival for the patients with non-metastatic osteosarcoma:
derivation cohort (n=158)

Figure 2A{[}iii{]}: Figure 2A{[}iii{]} is a Kaplan-Meier curve depicting
event-free survival for the patients with non-metastatic osteosarcoma:
derivation cohort (n=500)

Figure 2B: Figure 2B is a nomogram depicting the prognostic model for
non-metastatic Ewing's Sarcoma

Figure 2C: Figure 2C is a nomogram depicting the prognostic model for
metastatic osteosarcoma

Figure 2D: Figure 2D is a nomogram depicting the prognostic model for
metastatic Ewing's Sarcoma

\textbf{Hosted file}

\verb`figures prognosticators.pptx`  available at \url{https://authorea.com/users/757925/articles/731067-prognostic-modeling-for-bone-sarcomas-based-on-a-large-prospective-cohort-from-a-tertiary-care-cancer-centre-in-india}

\textbf{Hosted file}

\verb`tables prognosticators.docx`  available at \url{https://authorea.com/users/757925/articles/731067-prognostic-modeling-for-bone-sarcomas-based-on-a-large-prospective-cohort-from-a-tertiary-care-cancer-centre-in-india}

\selectlanguage{english}
\FloatBarrier
\end{document}

