RADIomics to Predict HER2 Status And T-DXd Efficacy in Metastatic Breast Cancer: the RADIOSPHER2 Study
Conditions: Radiomic, Radiomics, Breast Cancer Metastatic, Breast Cancer Stage IV, Breast Carcinoma, Breast Neoplasms, Breast Cancer, Breast Cancer With Bone Metastasis, Breast Cancer With Metastatic Bone Disease, Trastuzumab, HER2, HER2 + Breast Cancer, HER2 Positive Breast Carcinoma, HER2-positive Breast Cancer, Her2/Neu Positive Advanced Solid Tumors, HER2/Neu-positive Breast Cancer
Sponsor: Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

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Observational Retrospective-Prospective Study Protocol 
Study title 
RADIomics to predict HER2 status And T-DXd efficacy in metastatic breast 
cancer: the RADIOSPHER2 study. 
Sponsor 
Fondazione IRCCS Istituto Nazionale dei Tumori, via G. Venezian, 1 – 20133 
Milano 
Study 
investigators 
PI: Claudio Vernieri 
Co-investigators: Leonardo Provenzano, Francesca Ligorio, Giuseppe Fotia, 
Andrea Vingiani, Gabriella Greco, Giuseppina Calareso, Margherita Ruggirello, 
Raffaella Vigorito, Margherita Favali, Vanja Miskovic, Arsela Prelaj, Alessandra 
Laura Pedrocchi 
Background and 
Rationale 
HER2-low and BC. Breast cancer (BC) is the first cancer in terms of incidence 
worldwide, with a critical impact on public health due to its global disease 
burden.1 Historically, BC is classified in three major histopathological 
categories based on the expression of Hormone Receptors (HR) and Human 
Epidermal growth factor Receptor 2 (HER2) expression. While HER2-positive 
subtype 
is 
characterized 
by 
expression 
defined 
as 
3+ 
by 
immunohistochemistry (IHC) or 2+ with in situ hybridization (ISH) 
amplification, the other two subtypes, defined as HR+/HER2- and triple-
negative BC (TNBC), are characterized by a low (1+ or 2+, not ISH amplified) or 
null HER2 expression on cells surface, and so classified as “HER2-low” or 
“HER2-0”, respectively. As neither HER2-low nor HER2-0 BC subgroups 
demonstrated any survival benefit from the addition of HER2-directed 
monoclonal antibodies to chemotherapy in historical trials, these patients kept 
to be treated accordingly to HR status, without HER2-directed therapies.2,3 
Trastuzumab-Deruxtecan in mBC. Recently, the development of antibody 
drug conjugates (ADCs) revolutionized the landscape of treatment for BC pts 
with both HER2-positive and previously defined “HER-2 negative” disease. In 
particular, Trastuzumab Deruxtecan (T-DXd), an ADC composed of an anti-
HER2 antibody conjugated with a potent topoisomerase I inhibitor as cytotoxic 
payload, demonstrated impressive activity in pre-treated HER2-positive mBC, 
with a median Progression-Free Survival (mPFS) of 28.8 months as second-line 
therapy.4-6 Based on the intrinsic characteristics of T-DXd, in particular the 
potent chemotherapy (ChT) payload and the demonstrated bystander effect, 
the randomized phase III trial Destiny-Breast 04 was designed to investigate 
the efficacy of T-DXd on HER2-low metastatic BC (mBC), who received 1-2 prior 
lines of ChT for metastatic disease.7 T-DXd demonstrated superior efficacy 
compared to the control arm, reaching a mPFS of 9.9 months and OS of 23.4 

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months, thus becoming the preferred treatment option for most pts with 
HR+/HER2- aBC and HER2-low status progressing on first-line cycline-
dependent kinase 4/6 inhibitors (CDK4/6i) in combination with endocrine 
therapy (ET) and at least one line of ChT. The ongoing Destiny-Breast 06 trial 
will provide answers about the use of T-DXd as second line treatment after 
ET+CDK4/6i for HR+ mBC. The recently published DAISY trial was a prospective, 
phase 2, clinical trial assessing T-DXd efficacy in pts with mBC and different 
HER2 expression.8 Notably, differently from pivotal studies, a tumor specimen 
collected within 3 months before inclusion was mandatory. Among the three 
cohort, i.e. HER2 overexpressed (N = 72), HER2 low (N = 74) and HER2 IHC 0 (N 
= 40), the ORR, which was the primary endpoint of the trial, was 70.6%, 37.5% 
and 29.7%, respectively. Interestingly, also the mPFS seemed to be 
significantly proportional to the level of HER2 expression (11.1 months, 6.7 
months and 4.2 months in the three subgroups, respectively). This observation 
suggests that the HER2 expression level, if assessed at treatment baseline, can 
predict differential benefit from T-DXd. Finally, the activity of T-DXd in the 
HER2 0+ population could reflect the limitation of HER2 assessment, perhaps 
due to the insufficient sensitivity of IHC to low ranges of HER2 expression, the 
low capacity of pathologist to detect HER2 ultra-low expression, the spatial 
intratumor heterogeneity, or a combination of these factors. 
Biology of HER2-low mBC. From a biological point of view, it remains unclear 
if HER2-low BC is characterized by a peculiar biology compared to HER2-0 BC, 
reflecting in different clinical behaviour and impact on prognosis.9-10 In 
general, compared to HER2-0, HER2-low BC has been observed to have higher 
prevalence of PIK3CA mutation, lower prevalence of TP53 mutation, higher 
ERBB2 mRNA expression, less responsivity to neoadjuvant ChT and slightly 
better survival compared to HER2-0. However, a strong association between 
HER2-low expression and HR positivity has been observed, with HER2-low BC 
characterized by a higher HR expression rate, likely as a consequence of the 
HR-HER2 molecular crosstalk. Therefore, after adjusting to HR status, the 
abovementioned differences among the groups seem to be lost.11 However, 
some prognostic effect conferred by HER2 low status seems to be evident in 
some subgroups, such as HR+/HER2- BC treated with first-line CDK4/6i with ET, 
where a potential detrimental effect of low HER2 expression on survival was 
observed.12 
HER2 heterogeneity. HER2 expression in mBC is characterized by a strong 
heterogeneity both among different metastases and even at intratumor 

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level.13 In particular, intratumoral heterogeneity, which refers to the co-
existence of multiple tumor cell subpopulations with varying HER2 statuses 
within the same tumor, is not a rare phenomenon in BC, with a prevalence up 
to 40%. This heterogeneity, defined by different levels of protein expression 
or gene amplification evaluated by pathologists on tumor slides, has obvious 
clinical implication: firstly, a lower rate of pathologic complete response was 
observed in patients with documented HER2 heterogeneity underwent anti-
HER2-based neoadjuvant treatment; furthermore, it may contribute to 
inaccurate assessment of HER2 status and lead to inappropriate treatment 
regimens.14-15 HER2 heterogeneity at macroscopic level is less known, however 
HER2 different metabolic expression among metastatic sites was documented 
by HER2-positron emission tomography (PET)/computed tomography (CT) 
with (89) Zr-trastuzumab in patients enrolled in the prospective ZEPHIR trial.16 
HER2 is also characterized by a deep dynamicity over time (temporal 
heterogeneity), with up to 35% of discordance between paired primary and 
metastatic tumors, due to the selection of HER2-negative clones induced by 
treatment, genetic shift or analytic issues. 
Application of AI-based methodology for BC care. Artificial Intelligence (AI) 
recently emerged as a new methodologic approach to medical images.17 In 
particular, radiomics and pathomics are promising fields of application, 
identifying relevant features not accessible by raw human eye from 
radiological images and from pathology slides, respectively.18 In the last two 
years, a plethora of works demonstrated the feasibility and ability of radiomics 
in predicting patient outcomes, tumor response to therapies and in identifying 
molecular subtypes of human malignancies that are commonly identified 
through the use of specific omics analysis performed in tumor specimens, such 
as genomics, transcriptomics and proteomics.19-21 However, none of these 
tools are currently used in clinical practice. Two studies already demonstrated 
the feasibility of the radiomics use on predicting the benefit of mBC patients 
from novel therapeutics. In particular, Khorrami M et al built a CT scan-based 
radiomic model able to predict survival benefit in mBC with liver metastases 
treated with CDK4/6i in combination with endocrine therapy, reaching a 
notable accuracy (AUC = 0.77).19 Indeed, Zhao J et al conducted a retrospective 
radiomic analysis on a multicentre cohort of 240 mBC treated with 
immunotherapy to predict Disease Control Rate, with excellent results (AUC = 
0.92 in the validation set) and a discriminatory capacity superior to the clinical 
model.20 

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Radiogenomics aims to predict, through highly complex analysis of radiological 
images, those molecular alterations that could otherwise be reached only by 
high-costly analyses on tumor samples. This use of AI comes with several 
advantages: a) it allows the monitoring of tumor evolution over time and its 
temporal heterogeneity, avoiding repeated and potentially risky biopsy 
procedures (which are not always feasible); b) it allows the assessment of the 
inter-lesion tumor heterogeneity through the evaluation of all metastatic 
lesions at the same time, which could not be individually biopsied; c) it is a 
ready-to-use and easy-to-access tool, which could reduce the economic 
burden and save time required for extensive molecular analyses. 
Rationale. HER2 status may be heterogeneous across tumor lesions of the 
same pt (inter-lesion heterogeneity) and can also evolve during disease history 
(temporal heterogeneity).22-26 Therefore, to understand if a patient could 
benefit from HER2-directed therapies, including T-DXd, it is important to 
obtain HER2 status assessment close to the treatment start and possibly from 
different disease sites. However, re-biopsy is not always feasible and, even 
when feasible, not all disease sites can be approached. In addition, HER2 status 
by pathological assessment is characterized by several limitations (analytic 
issues, spatial heterogeneity) and re-biopsy is often not feasible in clinical 
practice (or associated with risky procedures, e.g. bone metastases).27 
Therefore, there is an urgent need to find a non-invasive, reliable and low-cost 
method capable of providing quantitative HER2 status assessment and, 
consequently, predictive information of potential benefit from HER2-directed 
therapies, in mBC patients. 
Based on these data, we hypothesized that innovative Machine Learning and 
Deep Learning-based radiogenomics methodologies can allow an updated and 
comprehensive evaluation of HER2 status in pts with mBC, breaking down the 
barrier of spatial and temporal heterogeneity. To test this hypothesis, we aim 
at identifying a radiomics score as surrogate of pathological-assessed HER2 
status and, finally, at improving the performance of actual HER2 assessment in 
predicting pts benefit form T-DXd treatment. 
Study design 
RADIOSPHER2 study is a monocentric, retrospective, observational study 
aiming at identifying a radiomics signature able to predict HER2 expression (0 
vs low vs overexpression) and T-DXd efficacy in mBC patients. The study also 
encompasses translational analyses and inter-modal correlations in order to 
provide novel insights about HER2 spatial and temporal heterogeneity, at the 
macroscopic and microscopic levels.  

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In particular, to pursue the study aims it will be conducted different analyses, 
which are structured in the subsequent sections. Figure 1 summarize the 
whole study design. 
 
Section 1: Identification of the HER2 radiomic signature («radiobiopsy»). We 
hypothesize that several features extracted from radiological images can 
predict the HER2 status (positive vs low vs null) on different specific metastatic 
sites of mBC patients. Accordingly to the study rationale, it will chosen to 
analyse images from those metastatic sites usually difficulty to approach with 
biopsy, such as lung, liver, pleural and bone lesions. The population of this first 
part of the study will consists with a cohort of patients with mBC underwent a 
liver, lung, pleura or bone biopsy in the metastatic setting at “Fondazione 
IRCCS Istituto Nazionale Tumori” in Milan (INT) with available imaging (~500 
patients expected). This first analysis will consist of extracting and selecting 
features that will be included in the «radiobiopsy» model for HER2 status 
prediction. 
 
Section 2: T-DXd treatment efficacy prediction with «radiobiopsy» score. As 
previously underlined, a positive and significant association was seen between 
T-DXd clinical benefit (defined as mPFS) and HER2 status, if evaluated on tissue 
collected right before treatment start. The «radiobiopsy» model will be 
applied to retrospectively calculate HER2 status from the baseline scan of 
HER2+ or HER2-low mBC pts underwent any-line T-DXd treatment for 
metastatic disease. The T-DXd PFS of patients assigned to the three cohorts 
(HER2-0, HER2-low or HER2-overexpressed) based on «radiobiopsy» model 
will be then calculated and differences among groups will be tested with Cox 
regression analysis.  Clinical characteristics (e.g., number of therapy line for 
metastatic disease, HR status, HER2 IHC expression) of the same cohort will be 
extracted from clinical health records, in order to build a multivariate Cox 
regression model. In case of the HER2 status calculated with the «radiobiopsy» 
model would  demonstrate an insufficient accuracy in predicting T-DXd 
survival benefit, a new multimodal model will be build taking in account 
radiological baseline scan and clinical features. To this proposal, the same 
modality used for building the «radiobiopsy» model will be applied, adopting 
T-DXd survival outcomes (12-month survival status) as clinical outcome for 
classification. 
 

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Section 3: HER2 heterogeneity prediction. HER2 heterogeneity is frequent in 
BC and is associated with worse prognosis.28  To assess the HER2 spatial 
heterogeneity at intra-tumor microscopic level, pathology slides derived from 
biopsies of patients enrolled to build  the «radiobiopsy» model will be also 
reviewed from pathologists for HER2 heterogeneity evaluation and digitalized 
for pathomics analyses.29 The HER2 heterogeneity will be evaluated by 
pathologist based on IHC expression and distribution. Both Hematoxilin-Eosin 
(HE) and HER2 stained slides will be analysed for this purpose. After splitting 
the entire cohort in training (80%) and testing (20%), through the DL-based 
pathomic workflow described in the “methodology” section, the training 
cohort will be assigned to a heterogeneity class and pathomic model trained 
for heterogeneity classification. The validation cohort will be used to assess 
the performance of the model. 
In order to evaluate if radiomic features can predict HER2 heterogeneity at 
intra-tumor macroscopic level, a different radiomic model will be developed 
for this purpose, using the HER2 heterogeneity score assigned by pathologist 
in the previous task as outcome.  
In addition, in order to confirm and quantify inter-lesion HER2 spatial 
heterogeneity, the features included in the discovered «radiobiopsy» 
signature will be extracted and the HER2 status calculated for the other 
metastatic lesions. To reach this purpose, different lesions from the same CT 
scan exam included in Task 1 and 2 will be segmented using the following 
algorithm: (1) at least one lesion from the same organ different to the one 
used for model development, if available; (2) at least one lesion for each of the 
different metastatic organs involved, if present. A minimum of 3 lesions and a 
maximum of 5 lesions will be considered for this task. A correlation analysis 
between HER2 status assignment from different lesions of the same patients 
will be performed to quantify the prevalence of HER2 variability among lesions 
in the cohort. In order to clarify if HER2 heterogeneity at microscopic level is 
associated with inter-lesion heterogeneity, a single-patient correlations 
between the heterogeneity pathomic score and the inter-lesion radiomic 
heterogeneity will be performed.  
Finally, to test if the intratumoral and inter-lesion heterogeneity can be 
predictable of T-DXd efficacy, both intratumor heterogeneity and inter-lesion 
heterogeneity score will be correlated with survival outcomes of patients 
treated with T-DXd enrolled for Section 2 proposal. A Cox regression analysis 

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will be used to test the ability of these scores to predict different outcomes of 
patients treated with T-DXd. Section 3 proposal is summarized at Figure 2. 
 
Section 4: Explanation of agnostic features with multi-omic correlations. To 
have insight into the features included into the «radiobiopsy» model, 
transcriptomics analysis will be conducted through bulk-RNA sequencing from 
available tumor FFPE specimens of lung, pleural and liver biopsies (~100 
samples from INT). The differential expression of several genes and 
transcriptomic signatures with known prognostic/predictive relevance, such as 
immune and metabolic genes and signatures, will be assessed among samples 
with differently expressed features included in the model. This will allow to 
dissect tumor biology by finding the biological counterpart of agnostic 
radiomics features that would remain otherwise unexplained. In addition, to 
test if the «radiobiopsy» model is able to predict the HER2 status defined by 
transcriptomic profile, correlation between radiomics classes and single-gene 
ERBB2 expression, HER2-pathway associated signature and PAM50 
classification will be also performed. 
Study objectives The primary objectives of the study are: 
 To identify a radiomic signature («radiobiopsy») able to predict the 
HER2 status on a specific metastasic site of mBC patients based on 
radiologic images (CT scan, PET-FdG) 
 To test the accuracy of «radiobiopsy» in predicting PFS in a cohort of 
mBC patients treated with T-DXd. 
 To identify a pathomic-based model able to predict intratumoral 
HER2 heterogeneity on pathology slides of HER2-positive and HER2-
low mBC patients. 
 To test the concordance between intratumoral and inter-lesional 
HER2 heterogeneity in HER2-positive and HER2-low mBC patients. 
 To test the effect of inter-lesional heterogeneity on PFS mBC patients 
treated with T-DXd. 
 To correlate the features included in the radiomic model with the 
transcriptomics data obtain from bulk-RNAseq from biopsies, in order 
to explain agnostic features discovered with radiomics. 
 
Secondary objectives include: 

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 To identify a radiomic-based model able to predict intratumoral HER2 
heterogeneity on baseline radiological scan of HER2-positive and 
HER2-low mBC patients. 
 To characterize the prevalence of inter-lesional HER2 heterogeneity in 
pluri-metastatic BC patients. 
 To identify a new radiomic signature able to predict efficacy of T-DXd 
in metastatic HER2-positive metastatic BC patients. 
 To integrate radiomics with clinical information to finally create a tool 
able to predict HER2 status based on available pts characteristics (e.g. 
HER2 status on primary biopsy, ER status) and actual radiological 
images. 
 To test the accuracy of HER2 status predicted by «radiobiopsy» model 
to predict the efficacy of subsequent anti-HER2 therapy (Section 1 
cohort). 
Study 
population 
Section 1: 
The population of this first part of the study will consists with a cohort of 
patients with mBC underwent a liver, lung, pleural or bone biopsy in the 
metastatic setting at INT with available imaging (CT scan and/or PET-FdG scan), 
performed from 01Jan2005 to 01Jan2024. Patients will be excluded in the case 
of: unknown HER2 status; matched available imaging carried out more than 
three months before the biopsy procedure or before the last previous 
treatment interruption; node, soft tissue or other visceral as biopsy site. 
Patients underwent tumor biopsy in the context of surgery will be included 
(e.g. orthopaedic surgery to bone stabilization). 
 
Section 2: 
The study population for section 2 will include HER2+ or HER2-low mBC 
patients underwent any-line T-DXd treatment for metastatic disease. HER2 
status will be defined from the last tissue biopsy, but patients with only 
surgical specimen or diagnostic biopsy can be included as well. Patients 
without available baseline CT or PET scan or patients with a follow up less than 
6 months will be excluded. 
 
Section 3: 
For the proposal of this section, the same inclusion and exclusion criteria for 
Section 1 and 2 will be applied. For inter-lesional HER2 heterogeneity 

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evaluation task, patients with less than three metastatic sites will be excluded 
from the analysis. 
 
Section 4: 
Patients included in this section must reflect the same criteria adopted for 
Section 1, with these additional exclusion criteria: patients underwent bone 
biopsy; patients underwent biopsy before 2010. 
Methodologies 
Radiomics Methodology: A team of four radiologists with experience on CT 
scan/PET images segmentation (G.G., R.V., M.R., G.C.) will identify the Volume 
Of Interest (VOI) through a semi-automated 3D segmentation process 
performed with Syngo.via software. The VOI will be selected as the same lesion 
underwent diagnostic biopsy. Peritumoral area will be obtained through 
automated augmentation techniques (+5 mm, +10 and +15 mm); if the 
peritumoral area obtained crosses blood vessels, gas, bone, bile duct, or 
exceeded the organ edge, the contour will be manually modified to exclude 
this area. After image pre-processing through different techniques (e.g., gray 
discretization, intensity normalization, and voxel resampling), radiomic 
features will be extracted from VOIs and peritumoral areas using the 
PyRadiomics library.30 In order to prevent signature overfitting, dimensionality 
of features is reduced before signature construction, firstly excluding features 
with high intraclass correlation coefficient and significantly different between 
the two outcome groups as assessed by one-way analysis of variance (ANOVA). 
Least absolute shrinkage and selection operator (LASSO) regression and/or 
Maximum Relevance Minimum Redundancy (MRMR) will be then used for the 
selection of feature that will be included in the final model with 5-fold cross-
validation.31-32 The INT cohort will be splitted in a training (80%) and testing 
cohort (20%). Different standard machine learning classifiers, such as Random 
Forest, Multilayer perceptron, Logistic Regression, Support Vector Machine, 
CatBoost, AdaBoost, XGBoost, will be trained and evaluated for this task. AUC 
of training cross-validation and testing sets will be used to evaluate the 
diagnostic performance of radiomics signature in distinguishing HER2 classes 
(high vs. low vs. 0) and PFS classes during T-DXd therapy (<12 vs ≥12 months). 
Pathomics Methodology: Pathology slides analyzed for HER2 review (Task 1) 
will be digitalized with Leica Scanscope AT2 and stored as “.svs” files to obtain 
Whole Slide Images (WSIs). In addition to the HER2-stained slides, Hematoxilin 
and Eosin (HE) slides will be also digitalized for pathomics purposes. WSIs will 
be analysed through Slideflow, a Python package that provides a unified 

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Application Programming Interface for building and testing deep learning 
models for histopathology.33 After color normalization procedures, 
quantitative pathomics feature will be extracted using a DL approach. Model 
creation and performance evaluation will follow the same methodology 
discussed in the “Radiomics” workflow. 
Transcriptomics analysis methodology: After cellular RNA extraction, RNA 
quality check and preparation of libraries, RNA will be sequenced with the 
NovaSeq 6000 System. Counts will be analyzed to dissect differential gene 
expression among radiomics-defined HER2 classes both at single gene level – 
through differential gene expression analysis (with the Deseq2 method) – and 
at pathway level – through Gene Set Enrichment Analysis (GSEA), Over 
Representation Analyses (ORA) and Gene Set Variation Analysis (GSVA).34-35 
The pathway-level analysis will be carried out using the Bioconductor packages 
fgsea, gsva, and clusterprofiler, taking advantage of the HALLMARK, KEGG, and 
REACTOME gene sets available from the GSEA Molecular Signatures Database 
(http://www.gsea-msigdb.org/gsea/msigdb/collections.jsp). 
Intrinsic 
subtypes will be assessed through the Absolute Intrinsic Molecular Subtyping 
(AIMS) algorithm.36 
Biological evaluation on pathology specimen: To guarantee the univocal class 
attribution, a pathologist with experience on BC diagnosis (A.V.) will reassess 
the HER2 status of all the enrolled pts in a blinded manner, both from training 
and validation cohort (Task 1). Accordingly to the study proposal, the 
pathological HER2 status will be intended as categorical, in particular will be 
considered three classes: HER2 0 (HER2-0), HER2 low (HER2-L) and HER2 
overexpressed (HER2-E). The HER2 assessment will be performed according to 
the updated 2023 ASCO-CAP recommendations for HER2 testing.37 HER2 
heterogeneity will be assessed from the same pathologist in both quantitative 
and qualitative way. For quantitative evaluation, a measure of dispersion of 
the distribution between HER2 IHC classes will be calculated; for qualitative 
evaluation, an heterogeneity pattern between “clustered”, “mosaic” and 
“scattered” will be attributed to the sample.38 
Statistical considerations: Kaplan-Meier curves will be compared using the Cox 
proportional hazards model. AUC values of the models will be calculated using 
the Caret and pROC R package and compared using Delong’s test. The 
association between two continuous variables will be assessed using the 
Pearson’s correlation test. The continuous variables will be compared using 
Student’s t-test or one-way analysis of variance (ANOVA) followed by post-hoc 

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Tukey’s test, as appropriate. The optimal cutoff values will be determined to 
maximize the sum of sensitivity and specificity of each model. For multiple 
comparisons, P-values will be adjusted using Holm’s method. All statistical 
analyses will be performed using R software version 4.1.0. 
Informed 
consent 
Patients who are alive at the time of study conduction, will be asked to sign 
the informed consent for the treatment of personal data for research 
purposes. For patients included in the Section 4 cohort will be asked to provide 
consent for the use of available archival tumor material for research purposes. 
For patients who are not alive at the moment of study initiation and data 
collection, we will be unable to ask an informed consent for the use of personal 
data. In this case, our study will adhere to the rules for the protection of 
personal data (“Autorizzazione Generale al trattamento dei dati personali 
effettuata per scopi di ricerca scientifica – 1 marzo 2012”, published on 
“Gazzetta Ufficiale n. 72 del 26 marzo 2012”). 
Study timelines 
The study will start on 1st Mar 2024. Primary results on the first radiomics score 
are expected by 1st Jan 2025. Secondary analysis on external validation cohort 
are expected by 1st Jul 2025. Final results including intermodal correlations are 
expected by 1st Jan 2027. 
Funding 
This study has been funded by a grant provided by “Linea 4 di Ricerca 
Corrente” from “Fondazione IRCCS Istituto Nazionale dei Tumori” in Milan. 
References 
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FONDAZIONE IRCCS 
ISTITUTO NAZIONALE DEI TUMORI  
__________________________________________________________________________________ 
20133 Milano – Via Venezian, 1 
RADIOSPHER2 Study – Version 1.0_23.01.2024 
 
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FONDAZIONE IRCCS 
ISTITUTO NAZIONALE DEI TUMORI  
__________________________________________________________________________________ 
20133 Milano – Via Venezian, 1 
RADIOSPHER2 Study – Version 1.0_23.01.2024 
 
RADIOSPHER2 Study – Version 1.0 
Versione 1.0 del 01/12/2023 
 
               Pag. 13 di 13 
E’ vietata la riproduzione, con qualsiasi mezzo, compreso la fotocopia, 
non autorizzata dalla Fondazione IRCCS - Istituto Nazionale dei Tumori – Milano