Original Article
External Validation of a Multi-Institutional Retroperitoneal
Sarcoma Nomogram
Chandrajit P. Raut, MD, MSc1,2; Rosalba Miceli, PhD3; Dirk C. Strauss, MD4; Carol J. Swallow, MD, PhD5,6;
Peter Hohenberger, MD7; Frits van Coevorden, MD8; Piotr Rutkowski, MD9; Marco Fiore, MD10; Dario Callegaro, MD10;
Paolo G. Casali, MD11; Rick L. Haas, MD12; Andrew J. Hayes, MD4; Charles Honore, MD13; Amanda J. Cannell, BS5,6;
Jens Jakob, MD7; Milena Szacht, MD, PhD9; Mark Fairweather, MD1,2; Raphael E. Pollock, MD, PhD14; Sylvie Bonvalot, MD15;
and Alessandro Gronchi, MD10
BACKGROUND: A multi-institutional nomogram for predicting disease-free survival (DFS) and overall survival (OS) in patients with
primary retroperitoneal sarcoma (RPS) incorporating relevant prognostic factors not included in the American Joint Committee on
Cancer staging system for soft tissue sarcoma has been reported. The authors validated this nomogram with an independent, transatlantic cohort. METHODS: Data from patients with RPS who were undergoing definitive resection at 1 of 6 sarcoma centers in Europe
and North America (“validation set”) were used to validate a RPS nomogram developed from 3 other centers (“development set”).
The nomogram incorporated 6 variables: age, tumor size, grade, histologic subtype, multifocality, and quality of surgery. Nomogrampredicted probabilities were stratified into 6 subgroups and compared with observed outcomes. Discriminative ability was quantified
by Harrell C statistics. RESULTS: The validation and development sets included 631 and 523 patients, respectively, all of whom underwent surgical resection at the institutions represented. The 7-year DFS and OS rates for the validation set were 38% (95% confidence
interval, 34%-43%) and 58% (95% confidence interval, 53%-63%), respectively. All 6 nomogram variables were found to be independently prognostic. The corrected Harrell C statistics concordance index values for the validation set were 0.69 for DFS and 0.73 for
OS, which were similar to those for the development set, suggesting good calibration of the nomogram in the validation cohort. CONCLUSIONS: The RPS nomogram was externally validated using a larger, independent cohort. The nomogram can be generalized to
patients undergoing surgery for RPS by specialized sarcoma surgeons at sarcoma centers. The nomogram provides a more individualized and disease-relevant estimation of OS compared with the American Joint Committee on Cancer classification. Cancer
C 2016 American Cancer Society.
2016;122:1417-24. V
KEYWORDS: grade, histology, multifocality, nomogram, retroperitoneal sarcoma, staging, surgery.
INTRODUCTION
Retroperitoneal sarcomas (RPS) account for approximately 15% of all soft tissue sarcomas (STS).1 Unique characteristics of
RPS make traditional staging using the 7th edition of the American Joint Committee on Cancer (AJCC) system a challenge.
Histologic subtype, multifocality, and site of origin are proven risk factors that are prognostic of outcome in patients with
STS, but are not included in the AJCC TNM staging for STS or stage grouping.2-5 Furthermore, the unique locoregional
and distant patterns of disease recurrence for specific RPS histologies are not reflected in the stage-based outcomes. Finally,
the majority of RPS measure 15 to 20 cm in size, and only approximately 6% of RPS reported in the Surveillance, Epidemiology, and End Results program measure <5 cm in size (the key discriminatory size used in AJCC STS staging).6,7 This,
Corresponding author: Chandrajit P. Raut, MD, MSc, Division of Surgical Oncology, Department of Surgery, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115; Fax: (617) 394-3052; craut@bwh.harvard.edu
1
Division of Surgical Oncology, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts; 2Center for Sarcoma and
Bone Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts; 3Department of Medical Statistics, Biometry and Bioinformatics,
IRCCS Foundation National Cancer Institute, Milan, Italy; 4Department of Surgery, Royal Marsden Hospital NHS Foundation Trust, London, United Kingdom;
5
Department of Surgical Oncology, Mount Sinai Hospital and Princess Margaret Cancer Centre, Toronto, Ontario, Canada; 6Department of Surgery, University of
Toronto, Toronto, Ontario, Canada; 7Division of Surgical Oncology and Thoracic Surgery, Mannheim University Hospital, Mannheim, Germany; 8Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands; 9Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie
Memorial Cancer Center and Institute of Oncology, Warsaw, Poland; 10Department of Surgery, IRCCS Foundation National Cancer Institute, Milan, Italy; 11Department of Cancer Medicine, IRCCS Foundation National Cancer Institute, Milan, Italy; 12Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam,
The Netherlands; 13Department of Surgery, Gustave Roussy Institute, Villejuif, France; 14Division of Surgical Oncology, Department of Surgery, Ohio State University Medical Center, Columbus, Ohio; 15Department of Surgery, Curie Institute, Paris, France
Presented at the 18th European Cancer Congress (ECCO)-40th European Society for Medical Oncology (ESMO) European Cancer Congress; September 25-29,
2015; Vienna, Austria.
DOI: 10.1002/cncr.29931, Received: December 15, 2015; Accepted: January 7, 2016, Published online February 24, 2016 in Wiley Online Library
(wileyonlinelibrary.com)
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Original Article
coupled with the finding that all RPS are by definition
“deep,” means that approximately 94% of RPS are by definition T2b tumors, and therefore tumor grade is the only
variable that discriminates the majority of RPS into the discrete stages of IIB and III.
Nomograms provide a more disease-specific and clinically relevant prognostic model for predicting various outcomes, including overall survival (OS), disease-free survival
(DFS), and local recurrence-free survival rates within a specific time frame compared with the AJCC system, particularly for STS. An initial STS nomogram from the
Memorial Sloan Kettering Cancer Center (MSKCC)
incorporated a variety of histologies and sites of origin.5
Although RPS were included, RPS/intra-abdominal sarcomas accounted for only 13% of the original development
cohort. The limited usefulness of the AJCC STS staging
system for RPS was a critical impetus to the eventual development of the initial single-institution, RPS-specific
nomograms from high-volume sarcoma centers.2,4 These
were critical in the evolution of expert opinion that has supported the use of such nomograms. However, a shortcoming of most single-institution nomograms is that they may
reflect the treatment biases of experienced sarcoma centers
and thus may not be more broadly applicable.
The first multi-institutional RPS nomogram (to the
best of our knowledge) was developed from patients undergoing surgical resection of primary RPS between 1999 and
2009 at the sarcoma centers at the National Cancer Institute in Milan, Italy; The University of Texas MD Anderson
Cancer Center in Houston, Texas; and the University of
California at Los Angeles and was externally validated using
another data set from 2001 to 2010 from the Gustave
Roussy Institute (Villejuif, France).8 Unlike prior nomograms, this one included size and age as continuous rather
than categorical variables, as well as relevant RPS histologies, tumor grade, multifocality, and completeness of surgical resection. The 7-year DFS and OS rates could be
predicted with accuracy (Fig. 1 A and B). This provided a
more broadly applicable nomogram.
This multi-institution RPS nomogram is currently
under consideration for inclusion in the forthcoming 8th
edition of the AJCC staging system. To determine
whether adoption of this nomogram by the AJCC is justified, we decided to critically evaluate the nomogram by
externally validating it with a larger data set collected from
other high-volume sarcoma centers.
in 6 countries from January 2002 through December
2011 were collected (“validation set”). Patients who had
undergone macroscopically incomplete initial surgical
resection of RPS at an outside referring institution were
excluded. Patients with retroperitoneal presentations of
gastrointestinal stromal tumor, desmoplastic small round
cell tumor, extraosseous Ewing sarcoma/primitive neuroectodermal tumor, uterine sarcoma histologic subtypes,
alveolar/embryonal rhabdomyosarcoma, and desmoid
fibromatosis were excluded.
Histologic subtypes incorporated within the multiinstitutional RPS nomogram included well-differentiated
liposarcoma, dedifferentiated liposarcoma, leiomyosarcoma,
unclassified/undifferentiated pleomorphic sarcoma, malignant peripheral nerve sheath tumor, solitary fibrous tumor,
and other. Liposarcomas were classified as “well differentiated” if only a well-differentiated component and no
dedifferentiated component was present and as
“dedifferentiated” if such a component was present regardless of tumor extent. All tumors were graded using the Federation Nationale des Centres de Lutte Contre le Cancer
(FNCLCC) (National Federation of Centers for the Fight
Against Cancer) grading system.9 The one treatment variable that was included in the nomogram was extent of surgical resection, with macroscopically complete (R0, negative
microscopic margin; R1, positive microscopic margin) surgical resections classified as “complete” and macroscopically
incomplete (R2) surgical resections classified as
“incomplete.” Patients were followed prospectively with
clinical examination and routine imaging (usually computed
tomography scans of the chest, abdomen, and pelvis) every 3
months to 4 months for the first 2 years, every 6 months for
the following 3 years, and yearly thereafter. Local and distant
disease recurrence and death were recorded as events.
The multi-institutional RPS nomogram incorporated 6 clinical characteristics and treatment variables to
predict DFS and OS: age (in years) at the time of diagnosis as a continuous variable (OS only), tumor size (in centimeters) as a continuous variable, FNCLCC grade (1, 2,
or 3), histologic subtype (well-differentiated liposarcoma,
dedifferentiated liposarcoma, leiomyosarcoma, unclassified/undifferentiated pleomorphic sarcoma, malignant
peripheral nerve sheath tumor, solitary fibrous tumor,
and other), multifocality at the time of initial presentation
(yes/no), and extent of surgical resection (complete,
incomplete [for OS only]).8
MATERIALS AND METHODS
Data regarding patients with primary RPS undergoing
surgery with curative intent at 6 dedicated sarcoma centers
Statistical Analysis
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Cohort characteristics, treatment variables, and outcomes
of the validation set were compared with the original
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RPS Nomogram Validation/Raut et al
Figure 1. Nomogram for 7-year (A) disease-free survival (DFS) and (B) overall survival (OS) in patients with primary retroperitoneal sarcoma. FNCLCC indicates Federation Nationale des Centres de Lutte Contre le Cancer (National Federation of Centers for
the Fight Against Cancer); Lipo DD, dedifferentiated liposarcoma; Lipo WD, well-differentiated liposarcoma; LMS, leiomyosarcoma; MPNST, malignant peripheral nerve sheath tumor; SFT, solitary fibrous tumor; UPS, unclassified/undifferentiated pleomorphic sarcoma. Reprinted from Gronchi A, Miceli R, Shurell E, et al. Outcome prediction in primary resected retroperitoneal soft
tissue sarcoma: histology-specific overall survival and disease-free survival nomograms built on major sarcoma center data sets.
J Clin Oncol. 2013;31:1649-1655 with permission from American Society of Clinical Oncology.8
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Original Article
TABLE 1. Demographic, Clinical, and Histologic Characteristics of Patients in the Retroperitoneal Sarcoma
Nomogram Development and Validation Sets
Variable
Median age, y
Median tumor size, cm
FNCLCC grade
Histologic subtype
Multifocality
1
2
3
Missing data
LMS
DDLPS
WDLPS
MPNST
UPS
SFT
Other
Missing data
No
Yes
Development Set
N 5 523 (%)
Validation Set
N 5 631 (%)
57 (IQR, 48–67; range, 16–88)
16 (IQR, 10–26; range, 2–75)
147 (28.1)
122 (23.3)
254 (48.6)
0
92 (17.8)
155 (30.0)
121 (23.4)
16 (3.1)
70 (13.5)
26 (5.0)
37 (7.2)
6
490 (93.7)
33 (6.3)
59 (IQR, 49–68; range, 18–95)
21 (IQR, 13–30; range, 1–65)
193 (32.7)
235 (39.8)
162 (27.5)
41
137 (21.7)
231 (36.6)
158 (25.0)
17 (2.7)
18 (2.9)
33 (5.2)
37 (5.9)
0
565 (89.5)
66 (10.5)
P
.248
<.0001
<.0001
<.0001
0.016
Abbreviations: DDLPS, dedifferentiated liposarcoma; FNCLCC, Federation Nationale des Centres de Lutte Contre le Cancer (National Federation of Centers for
the Fight Against Cancer); IQR, interquartile range; LMS, leiomyosarcoma; MPNST, malignant peripheral nerve sheath tumor; SFT, solitary fibrous tumor; UPS,
unclassified/undifferentiated pleomorphic sarcoma; WDLPS, well-differentiated liposarcoma.
“development set” from which the nomogram was constructed using the Mann-Whitney-Wilcoxon test or the
chi-square test when continuous or categorical variables,
respectively, were involved in the comparison.
OS and DFS curves were estimated using the
Kaplan-Meier method. OS was computed as the interval
between the date of surgery and the date of death from
any cause. DFS was computed as the interval between the
date of surgery and the date of first disease recurrence
(regardless of whether it was local, distant, or both) or
death from any cause, whichever occurred first.
External validation was performed by applying the
nomogram to the patients in the validation set. Calibration plots were used to compare nomogram-predicted
probabilities with observed outcomes; in a perfectly calibrated nomogram, the observed and predicted outcomes
would align along the 458 line of the calibration plot. The
discriminative ability of the nomogram was quantified by
the Harrell C statistics.
The statistical analyses did not include patients with
missing values among the nomogram variables and were
conducted using SAS statistical software (SAS Institute
Inc, Cary, NC) and R statistical software (R Foundation,
Vienna, Austria; http://www.r-project.org/)
RESULTS
The validation set included 631 patients who underwent
surgical resection with curative intent for primary RPS at 1
of 6 sarcoma centers: 1) Mount Sinai Hospital/Princess
Margaret Cancer Centre at the University of Toronto (To1420
ronto, Ontario, Canada); 2) Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School
(Boston, Mass); 3) Royal Marsden Hospital NHS Foundation Trust (London, UK); 4) Mannheim University Hospital (Mannheim, Germany); 5) Netherlands Cancer Institute
(Amsterdam, the Netherlands); and 6) Maria SklodowskaCurie Memorial Cancer Center and Institute of Oncology
(Warsaw, Poland). Cohort variables for the original development set and validation set are compared in Table 1. The
validation set was characterized by a larger median tumor
size (21 cm vs 16 cm; P<.0001), more patients with grade 2
tumors (40% vs 23%), and fewer patients with grade 3
tumors (27% vs 49%) (overall P<.0001). Treatment details
are summarized in Table 2. Completeness of surgical resection and use of radiotherapy were similar between the 2
cohorts, but the validation set included fewer patients
treated with chemotherapy (9% vs 40%; P<.0001).
Outcomes are summarized in Table 3. The median
follow-up in the validation set was 58 months (interquartile range, 36-87 months). Rates of local disease recurrence, distant disease recurrence, death, and death
without disease recurrence were similar in the validation
and development cohorts. Figure 2 shows the DFS and
OS curves for both the development and validation sets.
In the validation set, the 7-year DFS rate was 38.4%
(95% confidence interval [95% CI], 33.9%-43.4%) and
the 7-year OS rate was 58.0% (95% CI, 53.1%-63.4%);
the corresponding estimates in the development set were
35.7% (95% CI, 30.3%-42.1%) and 50.5% (95% CI,
44.4%-57.4%), respectively.
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RPS Nomogram Validation/Raut et al
TABLE 2. Treatments for Patients in the Development and Validation Sets
Development Set
N 5 523 (%)
Variable
Extent of resection
Complete (R0/R1)
Incomplete (R2)
Yes
No
Yes
No
Chemotherapy
Radiotherapy
475
48
207
316
193
330
(90.8)
(9.2)
(39.6)
(60.4)
(36.9)
(63.1)
Validation Set
N 5 631 (%)
598
33
57
574
200
431
(94.8)
(5.2)
(9.0)
(91.0)
(31.7)
(68.3)
P
.013
<.0001
.073
Abbreviations: R0 resection, macroscopically complete surgical resection with negative microscopic margins; R1 resection, macroscopically complete surgical
resection with positive microscopic margins; R2, macroscopically incomplete surgical resection.
TABLE 3. Patient Outcomes in the Development
and Validation Sets
Median follow-up,
mo
Recurrences
Local
Distant
Deaths
Deaths without
recurrence
Development
Set
N 5 523 (%)
Validation
Set
N 5 631 (%)
45
(IQR, 22–72)
130 (24.9)
94 (18.0)
171 (32.7)
34 (6.5)
58
(IQR, 36–87)
161 (25.5)
117 (18.5)
205 (32.5)
52 (8.2)
P
<.001a
.092b
.377b
.009c
.568b
Abbreviation: IQR, interquartile range.
a
P value at log-rank test for the comparison of the “reverse Kaplan-Meier”
curves.
b
P value at Gray test for the comparison between the crude cumulative
incidence curves of local recurrence, distant metastasis, and deaths without recurrence.
c
P value at log-rank test for the comparison of the Kaplan-Meier curves.
In the validation set, the Harrell C statistic concordance index values were very similar to those obtained in
the development set for both DFS and OS (DFS: 0.69 vs
0.71 [Fig. 3 A]; OS: 0.73 vs 0.74 [Fig. 3 B]).
DISCUSSION
Correlation between observed and nomogram-predicted
DFS and OS rates suggests good calibration of the multiinstitutional RPS nomogram in a larger, independent validation cohort.8 The data presented here demonstrate that
the nomogram applies well to patients undergoing surgery
for primary RPS performed by specialized sarcoma surgeons at sarcoma centers. Because the data are based on
outcomes after surgical resection of primary RPS, this
nomogram is not applicable to patients who have undergone surgical resection for recurrent disease, unlike the
single-institution nomogram from The University of
Texas MD Anderson Cancer Center.4 Furthermore,
unlike the recently reported RPS nomogram from
MSKCC, the multi-institutional RPS nomogram validated in the current study does not incorporate vascular
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resection or association with a prior radiation field as independent variables because these were very rare in the development set.3 In addition, this international, multiinstitution RPS nomogram includes age and tumor size as
continuous rather than categorical variables; the latter is
true of some of the single-institution nomograms.3,4
Although the relevance or advantage of such detail may be
debated, we have found that some of the largest tumors
(>30 cm) may have a better prognosis than those measuring in the range of 20 to 30 cm, reflecting a more favorable tumor biology in tumors that have reached such a
large size likely through prolonged indolent growth.
As a nomogram constructed based on data from multiple institutions and validated by other additional institutions, this nomogram has broad applicability. Nevertheless,
it reflects predicted outcomes in patients undergoing such
procedures by experienced sarcoma surgeons and multidisciplinary teams at high-volume sarcoma centers. Data used
as part of the initial development set and the current validation set were pooled in a previously reported survival analysis.6 OS outcomes from that combined data set were
favorable when compared with OS reported in a Surveillance, Epidemiology, and End Results analysis, which
included surgical resections performed in a percentage of
regions in the United States, bearing in mind all the caveats
of such a post hoc comparison. Nevertheless, the nomogram in the current study establishes a benchmark against
which other institutional outcomes may be compared.
The multi-institutional RPS nomogram in the current study provides a more individualized and diseaserelevant estimation of OS than the AJCC classification.
The 7th edition of the AJCC classification essentially only
risk stratifies RPS based on grade alone (which still
remains the most important prognosticator) because
approximately 95% of RPS are T2b tumors. The current
multi-institutional RPS nomogram provides a more specific estimation of both DFS and OS and, based on the
corroboration of the external validation set reported
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Figure 2. Kaplan-Meier curves for (A) disease-free survival and (B) overall survival for the development (black) and validation
(red) sets.
Figure 3. Calibration plot for external validation of the (A) disease-free survival (DFS) and (B) overall survival (OS) nomograms.
Nomogram-predicted probabilities were stratified into 6 equally sized subgroups. For each subgroup, the mean predicted probability (x-axis) was plotted against the Kaplan-Meier probability observed in the validation cohort (y-axis). Vertical lines indicate
the 95% confidence intervals of the Kaplan-Meier estimates. The dashed line in each panel indicates the reference line along
which an ideal nomogram would lie.
herein, will be proposed for inclusion in the 8th edition of
the AJCC classification.
Nomograms have the flexibility to incorporate additional independent prognostic factors. The use of chemotherapy did not appear to impact outcome in the
nomogram used herein. There was a significantly lower
percentage of patients undergoing chemotherapy in the
validation set compared with the development set (9% vs
40%; P<.0001) (Table 2), reflecting institutional bias in
1422
default therapy for RPS. However, this did not appear to
affect the predictive value of the multi-institutional RPS
nomogram. The percentage of patients undergoing radiotherapy was similar in the 2 data sets. The ongoing phase
3 randomized study of preoperative radiotherapy plus surgery versus surgery alone for patients with retroperitoneal
sarcoma (STRASS) trial will define the standard of care
with respect to the potential benefit of preoperative radiotherapy for RPS. If found to be independently predictive
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RPS Nomogram Validation/Raut et al
of outcome, preoperative radiotherapy could be added as
a separate prognostic factor in the future.
There has been much debate recently concerning the
optimal extent of surgical resection.10-15 In the recent
single-institution nomogram reported by Tan et al, the
number of organs resected was incorporated.3 However,
in the multi-institutional RPS nomogram validated in the
current study, organ resection was not included because
we noted institutional variation in the number of organs
resected. This may well reflect not only different institutional practices, but also the broad variability of presentation of these tumors, which may be represented
differently in the various institutional case mixes. In the
series reporting combined outcomes from the majority of
the institutions in the development and validation sets,
the number of organs resected was not found to be an independent prognosticator.6 Thus, the number of organs
resected is not as predictive of the extent of surgical resection as in single-institution nomograms, which reflect a
more uniform treatment strategy.3 The number of organs
resected remains a prognosticator only as long as it reflects
a uniform approach to the removal of the disease, which
then still has to be tailored to the single patient.
Eventually, the DFS nomogram could be replaced
with separate nomograms for local and distant recurrencefree survival. Whether OS or disease-specific survival is a
more optimal measure of long-term outcome is a matter
of debate. A small percentage of patients in each of the development and validation cohorts died without developing disease recurrence (7% and 8%, respectively). Future
editions may include a disease-specific survival nomogram, as the recent study from MSKCC study reported.3
Conclusions
External validation of the multi-institutional RPS nomogram established its broad applicability in predicting outcomes in patients with primary RPS who were treated at
experienced centers, and supports its inclusion in the 8th
edition of the AJCC classification.
FUNDING SUPPORT
No specific funding was disclosed.
CONFLICT OF INTEREST DISCLOSURES
The authors made no disclosures.
AUTHOR CONTRIBUTIONS
Chandrajit P. Raut: Conceptualization, methodology, validation,
formal analysis, investigation, resources, data curation, writing–
original draft, writing–review and editing, visualization, supervision, and project administration. Rosalba Miceli: Methodology,
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May 1, 2016
software, validation, formal analysis, investigation, resources, and
writing–review and editing. Dirk C. Strauss: Conceptualization,
methodology, formal analysis, investigation, resources, data curation, and writing–review and editing. Carol J. Swallow: Conceptualization, methodology, formal analysis, investigation, resources,
data curation, and writing–review and editing. Peter Hohenberger: Conceptualization, methodology, formal analysis, investigation, resources, data curation, and writing–review and editing.
Frits van Coevorden: Conceptualization, methodology, formal
analysis, investigation, resources, data curation, and writing–review
and editing. Piotr Rutkowski: Conceptualization, methodology,
formal analysis, investigation, resources, data curation, and writing–review and editing. Marco Fiore: Conceptualization, methodology, validation, formal analysis, investigation, data curation, and
writing–review and editing. Dario Callegaro: Conceptualization,
methodology, validation, formal analysis, investigation, data curation, and writing–review and editing. Paolo G. Casali: Data curation and writing–review and editing. Rick L. Haas: Resources, data
curation, and writing–review and editing. Andrew J. Hayes:
Resources, data curation, and writing–review and editing. Charles
Honore: Resources and writing–review and editing. Amanda J.
Cannell: Data curation and writing–review and editing. Jens
Jakob: Resources and writing–review and editing. Milena Szacht:
Data curation and writing–review and editing. Mark Fairweather:
Resources, data curation, and writing–review and editing. Raphael
E. Pollock: Conceptualization, methodology, investigation, data
curation, and writing–review and editing. Sylvie Bonvalot: Conceptualization, methodology, formal analysis, investigation, resources, data curation, and writing–review and editing. Alessandro
Gronchi: Conceptualization, methodology, validation, formal
analysis, investigation, resources, data curation, writing–review and
editing, visualization, supervision, and project administration.
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