Assessment of spatial tumor heterogeneity using CT growth patterns estimated by tumor tracking on 3D CT volumetry of multiple pulmonary metastatic nodules
Assessment of spatial tumor heterogeneity using CT growth patterns estimated by tumor tracking on 3D CT volumetry of multiple pulmonary metastatic nodules
Jeongin Yoo 0 1
Semin ChongID 1
Changwon Lim 1
Miyoung Heo 1
In Gyu Hwang 1
0 Department of Radiology, Seoul National University Hospital , Seoul , Korea , 2 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul , Korea , 3 Department of Applied Statistics, Chung-Ang University , Seoul , Korea , 4 Division of Hematology/Oncology, Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine , Seoul , Korea
1 Editor: Mathieu Hatt, INSERM , FRANCE
Our purpose was to assess the differences in growth rates of multiple pulmonary metastatic nodules using three-dimensional (3D) computed tomography (CT) volumetry and propose a concept of CT spatial tumor heterogeneity. We manually measured the largest diameter of metastatic pulmonary nodules on chest CT scans, and calculated the 3D maximum diameter and the volume using a semi-automated 3D CT volumetry of each nodule. The tumor response was assessed according to the revised RECIST 1.1. We defined a nodule as an outlier based on 1.5 times growth during follow-up. The CT spatial tumor heterogeneity was statistically analyzed by the ?minimum combination t-test method? devised in our study. On manual measurement, the tumor response category was stable disease (SD) in all 10 patients. Of them, total 155 metastatic nodules (4-52 nodules per patient) were segmented using the 3D CT volumetry. In the 3D maximum diameter, 9 patients had SD except for one patient with partial response in the two selected nodules; for the volume, all 10 patients were SD. For the 3D maximum diameter, six patients had at least one outlier; whereas five patients had the outlier on the volume measurement. Six patients were proven to have overall CT spatial tumor heterogeneity.
Data Availability Statement: All relevant data are
within the manuscript.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
Materials and methods
The spatial tumor heterogeneity determined in a CT parametric approach could be
statistically assessed. In patients with CT spatial heterogeneity, tumors with different
growth rates may be neglected when the nodules are assessed according to the current
In the RECIST 1.1 revised in 2009, five target lesions were selected instead of 10, and a
maximum of two target lesions per organ were selected instead of five [
]. Measurable lesions were
defined as those with a longest diameter of at least 10 mm on computed tomography (CT)
with a section thickness of 5 mm or less, whereas non-measurable lesions were those with a
longest diameter of less than 10 mm [
]. However, many viable tumor cells are still present in
non-measurable lesions as well as non-target lesions. In addition, according to the concept of
tumor heterogeneity, which implies the coexistence of subpopulations of cancer cells that
differ in their genetic, phenotypic, or behavioral characteristics within a given primary tumor
and between a given primary tumor and its metastatic lesions, only two randomly selected
target lesions are not representative of the tumor burden [
]. This tumor heterogeneity may be
present within a given tumor, such that different regions of the tumor harbor different
repertoires of genetic aberrations (spatial heterogeneity), or during the course of disease progression
(temporal heterogeneity) [
]. Hence, assessing the overall tumor burden by using only two
lesions per organ has limited effectiveness, since pulmonary metastases frequently manifest as
more than two nodules [
Recently, precision medicine is an emerging field that focuses on identifying effective
treatment approaches for patients based on genetic, environmental, and lifestyle factors [
According to the concept of tumor heterogeneity, the gene mutation that occurs depending on
the time and location of the tumor causes tumor recurrence, and decreases the antitumor
therapeutic effect. Thus, the treatment and prevention based on precision medicine should include
the assessment of tumor heterogeneity using noninvasive and ethical methods. This study
attempted to review the possibility of considering spatial tumor heterogeneity in the course of
treatment of patients with multiple lung metastases by acquiring the 3D maximum diameter
and the volume of each lung nodule by using 3D CT volumetry.
In this study, we hypothesized that there would be a difference in the growth rates of all
metastatic nodules because of spatial tumor heterogeneity when patients have multiple
pulmonary metastatic nodules. Our purpose, therefore, was to measure the maximum diameter and
volume of all multiple but countable metastatic pulmonary nodules by using a semi-automated
three-dimensional (3D) volumetry software in each patient with pulmonary metastasis, assess
the differences in growth rates of each metastatic nodule during follow-up, and propose a
concept of CT spatial tumor heterogeneity.
The institutional review board of our institution approved this retrospective study and waived
the requirement for informed patient consent for inclusion in this study.
A search of our institutional electronic medical records database yielded the data of 189
patients with multiple pulmonary metastases who had no significant change in tumor growth
on follow-up chest CT after chemotherapy between January 2010 and December 2014. We
retrospectively reviewed their clinical and chest CT findings. Of these 189 patients, those with
less than 2 metastatic lung nodules or more than 60 (n = 92), or those with atelectasis due to
pleural effusion (n = 81) or pneumothorax (n = 13) were excluded. Finally, 10 patients (M:
2 / 12
F = 9:1; mean age, 66.9 years; range, 51?74 years) with multiple pulmonary metastases from
thoracic or non-thoracic malignant tumors such as small cell lung cancer (n = 2), renal cell
carcinoma (n = 3), maxillary sinus cancer (n = 1), colorectal adenocarcinoma (n = 3), and
cholangiocarcinoma (n = 1) were enrolled in this study.
All CT examinations were performed at our institute by using a 64-slice or 256-slice
multidetector CT system (Brilliance 64 or Brilliance iCT, respectively, Philips Healthcare). The scan
parameters included a tube voltage of 120 kV and tube current of 120 mAs at a pitch of 1.015
for the 64-slice scanner and 0.915 for the 256-slice scanner. Single-phase peripheral
intravenous power injection was performed using a total of 1.5 mL/kg of body weight of
iopamidolbased nonionic contrast media (Pamiray 370, Dongkook Pharmaceutical). All CT data were
reconstructed using a standard filter at a slice thickness of 1 mm.
A thoracic radiologist with 12 years of experience in thoracic imaging selected the two longest
metastatic nodules of each patient and manually measured the diameters of the nodules on
chest CT images by using an electronic caliper at the picture archiving and communication
system (Maroview 5.4, Infinitt). In all 10 patients, the sums of the two longest diameters were
used to assess the tumor response according to the response threshold based on the revised
RECIST 1.1 [
]. Complete response (CR) was defined as the disappearance of all lesions.
Progressive disease (PD) was defined as a more than 20% increase in the sum of the longest
diameters of the target lesions, and partial response (PR) as a more than 30% decrease in the sum of
the longest diameters of the target lesions. A patient who could not be classified as having
either PR or PD was diagnosed as having stable disease (SD).
3D CT volumetry using a semi-automated method
All CT image data were transferred onto a dedicated workstation by using 3D visualization
software (IntelliSpace Portal version 6.0, Philips Healthcare; EBW 4.5, Philips Healthcare) (Fig
1). Each patient?s metastatic nodules were automatically segmented using the ?Segmentation?
tool and manually edited on the axial CT images by a radiology resident. The segmentation
and selective editing was finally confirmed by the experienced thoracic radiologist. After
segmentation, the 3D maximum diameter (cm) and volume (cm3) of each nodule were
automatically measured. The former was calculated as the longest diameter that can be drawn in the 3D
volume, and the latter was calculated by counting the voxels in the contour. We calculated the
number of segmented nodules and the time interval between the two serial CT examinations.
We also calculated the change rates (%) of each nodule regarding both 3D maximum diameter
and volume in each 10 patients and demonstrated them in bar graphs.
The two largest and total values of each parameter were selected for assessing the tumor
response according to the revised RECIST 1.1 in all 10 patients. We calculated the change rate
(%) in the sums of each parameter?s largest values between the two time points. For the 3D
maximum diameter, the same response threshold as that for manual measurement was
applied. For a volume change threshold, PD was defined as a more than 73% increase in the
sum of the largest volume and PR as a more than 66% decrease in the sum of the largest
We conducted a threshold analysis to determine a nodule that was 1.5 times larger on
follow-up CT than on the previous CT, which was defined as an outlier. As change ratio criteria,
0.5 was adopted for 3D maximum diameters and 2.375 (calculated using the equation 1.53?13)
for volumes in order to determine the outlier nodule presumably having prominent growth
during the follow-up period.
3 / 12
We compared the difference of change rates between the selected two largest and total nodules
and between the 3D maximum diameter and volume using the paired t-test and the
Bland-Altman plot. Statistical analyses were performed using MedCalc (version 12.6, MedCalc
Software), PASW (version 18, SPSS), R (version 3.2.3) and Minitab (version 15, Minitab Inc.);
p < 0.05 was considered statistically significant.
A statistical analysis for the concept of spatial tumor heterogeneity was performed by using
the ?minimum combination t-test method? devised by a statistician involved in our study. In
this analysis, we considered the rate of change by using observations from two visits for the
two variables calculated as follows:
rate of change ?
Fig 1. Lesion segmentation and tumor tracking by semi-automated 3D CT volumetry of multiple pulmonary metastatic nodules. (a) Metastatic
pulmonary nodules are automatically segmented and manually edited on axial CT images of two time points using 3D visualization software. After
segmentation, the 3D maximum diameter and volume of each nodule are automatically calculated. (b, c) Segmented pulmonary metastatic nodules are seen in
3D reconstructed images of baseline (b) and follow-up (c) chest CT scans.
4 / 12
Under the assumption of no heterogeneity, we assumed that the average rates of change of
each lesion were equal, and hence, the change rates of each lesion can be assumed to follow the
same distribution. First, we divided the lesions of each patient into two groups of similar size
(e.g., 31 lesions for a patient were divided into two groups of 15 and 16 lesions each). We
considered all possible combinations of two groups, and not just certain combinations. For
example, the number of combinations of dividing 31 lesions into 15 and 16 is 31?15 = 300,540,195
(for the convenience of analysis, the number of combinations was limited to a maximum of
4,000). Since the lesions were then divided into two groups for each combination, we could
conduct a two-sample t-test for the rate of change. The hypotheses of the t-test were as follows:
H0 : m1 ? m2 vs: H1 : m1 6? m2
where ?1 and ?2 were the average rates of change of groups 1 and 2, respectively. After
conducting the two-sample t-test for a rate of change, if the p-value was less than the significance
level, the null hypothesis was rejected and we concluded that the average rates for the two
groups were different from each other. This finding suggested the existence of tumor
heterogeneity. Since we should consider all possible combinations, we conducted the t-test as many
times as the number of combinations, and hence, the same number of p-values was obtained.
If the minimum value of the p-values was less than the significance level of 0.05, we rejected
the overall null hypothesis of no heterogeneity. Thus, the existence of tumor heterogeneity
regarding each variable for a patient could be observed. Thereafter, we conducted the t-test for
the two rates of change and calculated two minimum p-values. Using those minimum
p-values, we determined whether there existed any tumor heterogeneity regarding the
corresponding variables for each patient. Lastly, if the minimum p-values were less than the significance
level of 0.05, we made an overall conclusion that there existed spatial tumor heterogeneity in a
The clinical and manual CT characteristics of all 10 patients are summarized in Table 1. In
these 10 patients, the mean time interval between the two time points was 70.3 days (range,
32?151 days). According to the RECIST 1.1, the overall tumor response was assessed as SD by
manual measurement in all 10 patients.
In all 10 patients, total 155 metastatic pulmonary nodules (mean, 15.5 nodules; range, 4?52
nodules) were segmented and analyzed using semi-automated 3D CT volumetry. Regarding
3D maximum diameter and volume, the change rates (%) of each nodule in each patient were
demonstrated in bar graphs (Fig 2). Table 2 summarizes the tumor response assessment
according to the RECIST based on the number of metastatic nodules (two largest versus total
nodules) measured by semi-automated 3D CT volumetry for the 3D maximum diameter and
the volume. Regarding the 3D maximum diameter, nine patients were assessed as having SD
in cases of both the two largest and all metastatic nodules, except for one patient (patient no.
4) who was assessed as having PR only in case of the two largest nodules but SD in case of all
metastatic nodules. Regarding the volume, all 10 patients were assessed as having SD
regardless of the number of selected nodules.
There was a statistically significant difference of the change rate between 3D maximum
diameters of two and total nodules (p = 0.013); whereas there were no statistical significances
in those between two and total nodule volumes (p = 0.464) and between the 3D maximum
diameter and the volume of either two or total nodules (p = 0.164 and 0.070, respectively). On
Bland-Altman plots, however, there were discrepancies between change rates of the
measurement values based on the number of selected nodules and the measurement method (Fig 3).
5 / 12
CT = computed tomography RCC = renal cell carcinoma SCLC = small cell lung cancer AD = adenocarcinoma
Sum 1 = the sum of diameters of two target nodules at the first CT examination
Sum 2 = the sum of diameters of two target nodules at the follow-up CT examination
a % change was calculated by the change rate of the sums of two largest values between the two time points based on RECIST 1.1. If the lesion grows, it is recorded as a
positive number. If the lesion gets shrinkage, it is recorded as a negative number. Zero means neither growing nor shrinkage. SD = stable disease PD = Progression of
Regarding the mean difference of change rates between two and total nodules, the limit of
agreement (i.e., mean ? 1.96 standard deviation) was wider on the volume measurement
(-46.85 and 60.05) than that on the 3D maximum diameter measurement (-12.33 and 36.49)
(Fig 3A and 3B). Regarding the mean difference of change rates between the measurement
values estimated by the 3D maximum diameter and the volume, the limit of agreement was
narrower in the selection of total nodules (-21.78 and 43.41) than that in the selection of two
nodules (-50.31 and 82.91) (Fig 3C and 3D).
The change ratio in each parameter between the two serial CT examinations is shown in
Fig 4. For the 3D maximum diameter, six patients had at least one more up to four metastatic
nodules with a change ratio of more than 0.5 (outliers); whereas the remaining four patients
had metastatic nodules with a change ratio of less than 0.5 (Fig 4A). Regarding the volume,
five patients had outliers with a change ratio of more than 2.375 (Fig 4B).
The CT spatial tumor heterogeneity of all metastatic nodules in each patient is shown in
Table 3. Six patients (patients no. 5?10) were proven to have overall spatial tumor
heterogeneity by using the minimum of the two minimum p-values less than 0.05, while the other four
patients (patients no. 1?4) had no overall spatial tumor heterogeneity. A minimum p-value
less than 0.05 was obtained in 6 patients (patients no. 5?10) for the 3D maximum diameter
and in three patients (patients no. 7, 9, and 10) for the volume, which was presumed to have
spatial tumor heterogeneity based on the measurement method.
Few studies have investigated the optimal number of target lesions required for the objective
assessment of tumor response. Marten et al. suggested that the assessment of tumor response
using volume criteria in pulmonary metastases should include a minimum of three target
lesions, on the basis of their analysis of five metastatic lesions [
]. In our study, we conducted
linear and volumetric quantification by using semi-automated 3D CT volumetry of more than
6 / 12
Fig 2. Change rates in 3D maximum diameter and volume of each nodule of 10 patients. Bar graphs show the change rates of each nodule in each patient regarding
3D maximum diameter (a) and volume (b). The x-axis of each patient?s graph means the number of nodules and the y-axis of each patient?s graph means the change rate
(%) of each nodule.
four metastatic lung nodules, which, to our knowledge, is the first study on tumor response
assessment by measuring all of the metastatic nodules (up to 52 nodules) in each patient. For
linear quantification of metastatic nodules based on the 3D maximum diameter estimated by
semi-automated 3D CT volumetry, there was discrepancy in overall response between the two
largest and all metastatic nodules in one patient (patient no. 4), whereas all 10 patients showed
7 / 12
Sum 1 = the sum of diameters or volumes of selected target nodules at the first CT examination
Sum 2 = the sum of diameters or volumes of selected target nodules at the follow-up CT examination
a % change was calculated by the change rate of the sums of the selected largest values between the two time points based on RECIST 1.1. If the lesion grows, it is
recorded as a positive number. If the lesion gets shrinkage, it is recorded as a negative number. Zero means neither growing nor shrinkage. SD = stable disease
PR = partial response
SD for volume quantification regardless of the number of nodules (Table 2). This discrepancy
may affect the oncologist?s decision to continue the current chemotherapeutic agent or to
replace it with another drug. Therefore, we think that determining whether the tumor
response to the two largest lesions or all of the lesions actually reflects a patient?s current
disease status is an important issue related to future treatment strategies and patient prognosis.
Recently, tumor heterogeneity is perceived as one of the causes of resistance to targeted
therapy, which can be generally evaluated by a genetic approach [
]. However, there are
practical limitations to the genetic evaluation of all metastatic nodules in each patient. In our study,
for the practical characterization of spatial heterogeneity, we devised a CT phenotypic
approach, which implied the measurement of morphologic changes of a nodule observed on
CT as phenotypes resulting from the expression of genes in a nodule. To analyze the presence
or absence of this CT spatial tumor heterogeneity statistically, we also devised the so-called
minimum combination t-test method. In our study, six patients were statistically proven to
have overall CT spatial tumor heterogeneity of metastatic nodules. Therefore, in patients with
spatial tumor heterogeneity assessed by the CT phenotypic approach, when only two target
lesions will be assessed according to the RECIST 1.1, it is not likely to represent a change in
overall tumor burden of metastatic nodules during chemotherapy. Hence, such cases might be
assessed by measuring all metastatic nodules based on the volume calculated using 3D CT
volumetry. This is a major clinical implication of our study, which recommends a change to
8 / 12
Fig 3. Bland-Altman plots of change rates between two time points according to the number of selected nodules and the measurement method. Plots show
differences of change rates between two and total nodules estimated by 3D maximum diameter (a) and volume (b) measurements. Plots show differences of change rates
between 3D maximum diameter and volume measurements in the selection of two (c) and total (d) nodules.
the current methodology of assessment of tumor response using the linear measurement of a
few target lesions.
In terms of primary tumors of the lung cancer, tumor heterogeneity has been assessed
noninvasively using variable imaging modalities and features. The texture analysis refers to a
variety of mathematical methods that can be used to evaluate the gray-level intensity and position
of the pixels within an image to derive texture features that provide a measure of intralesional
]. However, these texture analyses would not be applicable in cases of
multiple pulmonary metastatic nodules due to the lesion size and number. In our study, we found
out nodules that grow more than 1.5 times in 3D maximum diameters and volumes and
9 / 12
Fig 4. Change ratios between two time points estimated by linear and volumetric measurements. Scatter plots show change ratios of the 3D maximum
diameter (a) and volume (b) of each nodule between two serial CT examinations. Dotted lines represent the change ratio criteria defined as 0.5 for 3D
maximum diameter and 2.375 for volume in order to determine outlier nodules. Numbers above the dots are the nodule number given when performed
described them as outliers (Fig 4). We think that these outliers could have spatial tumor
heterogeneity which results in different growth rates and patterns among multiple metastatic nodules.
Nevertheless, our study has several limitations. First, our study included a small number of
patients. However, as our study focused on the growth rate of each lung nodule, the total
number of nodules would be more meaningful than the number of patients. We have applied a
minimal method to establish a hypothesis that metastatic nodules are heterogeneous within
each patient rather than heterogeneous in each patient, resulting in spatial tumor
heterogeneity. Second, our study population had diverse primary tumor entities and subsequently
underwent treatment using variable chemotherapeutic regimens. We think that the type of primary
tumor would have little effect on the results because our study was about the tumor response
assessment in patients with multiple pulmonary metastases, which have been reported to show
no interval change. Third, each nodule in our patients was not pathologically proven
metastatic and was not genetically confirmed to have spatial tumor heterogeneity. However, it is
not practically and ethically possible to perform a biopsy for all metastatic nodules at present.
10 / 12
Fourth, some of the follow-up intervals were relatively short. However, this is unlikely to affect
the evaluation of tumor response determined using two different measurement techniques.
The volume calculated using 3D CT volumetry might be more reliable than that calculated
using the traditional methods for tumor response assessment. In our study, spatial tumor
heterogeneity determined via the CT phenotypic approach could be statistically assessed using the
minimum combination t-test method. In patients with CT spatial heterogeneity, the outlier
tumor with a different growth pattern may be excluded when only two or some target lesions
are assessed according to the current guideline. Therefore, we expect that these outlier tumors
would be the emerging targets that necessitate a different treatment strategy in the future.
Conceptualization: Semin Chong, In Gyu Hwang.
Data curation: Changwon Lim, Miyoung Heo, In Gyu Hwang.
Formal analysis: Changwon Lim, Miyoung Heo.
Investigation: Jeongin Yoo, In Gyu Hwang.
Methodology: Jeongin Yoo.
Resources: In Gyu Hwang.
Supervision: Semin Chong.
Validation: Changwon Lim, Miyoung Heo.
Writing ? original draft: Jeongin Yoo, Semin Chong.
Writing ? review & editing: Semin Chong, Changwon Lim, In Gyu Hwang.
11 / 12
1. Eisenhauer E , Therasse P , Bogaerts J , Schwartz L , Sargent D , Ford R , et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) . Eur J Cancer . 2009 ; 45 ( 2 ): 228 - 47 . https://doi.org/10.1016/j.ejca. 2008 . 10 .026 PMID: 19097774
2. Therasse P , Arbuck SG , Eisenhauer EA , Wanders J , Kaplan RS , Rubinstein L , et al. New guidelines to evaluate the response to treatment in solid tumors . J Natl Cancer Inst . 2000 ; 92 ( 3 ): 205 - 16 . https://doi. org/10.1093/jnci/92.3.205 PMID: 10655437
3. Martelotto LG , Ng C , Piscuoglio S , Weigelt B , Reis-Filho JS . Breast cancer intra-tumor heterogeneity . Breast Cancer Res . 2014 ; 16 ( 3 ): 210 . https://doi.org/10.1186/bcr3658 PMID: 25928070
4. Murata K , Takahashi M , Mori M , Kawaguchi N , Furukawa A , Ohnaka Y , et al. Pulmonary metastatic nodules: CT-pathologic correlation . Radiology . 1992 ; 182 ( 2 ): 331 - 5 . https://doi.org/10.1148/radiology. 182.2.1732945 PMID: 1732945
5. Crow J , Slavin G , Kreel L . Pulmonary metastasis: a pathologic and radiologic study . Cancer . 1981 ; 47 ( 11 ): 2595 - 602 . https://doi.org/10.1002/ 1097 - 0142 ( 19810601 )47: 11 < 2595 : :aid-cncr2820471114>3.0 . co; 2 - q PMID : 7260854
6. Suzuki C , Jacobsson H , Hatschek T , Torkzad MR , Bode?n K , Eriksson-Alm Y , et al. Radiologic Measurements of Tumor Response to Treatment: Practical Approaches and Limitations . Radiographics. 2008 ; 28 ( 2 ): 329 - 44 . https://doi.org/10.1148/rg.282075068 PMID: 18349443
7. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease . Washington, DC: National Academies Press (US); 2011 .
8. Collins FS , Varmus H. A New Initiative on Precision Medicine . N Engl J Med . 2015 ; 372 ( 9 ): 793 - 5 . https://doi.org/10.1056/NEJMp1500523 PMID: 25635347
9. Marten K , Auer F , Schmidt S , Rummeny EJ , Engelke C . Automated CT volumetry of pulmonary metastases: the effect of a reduced growth threshold and target lesion number on the reliability of therapy response assessment using RECIST criteria . Eur Radiol . 2007 ; 17 ( 10 ): 2561 - 71 . https://doi.org/10. 1007/s00330-007-0642 -x PMID : 17492290
10. Turner NC , Reis-Filho JS . Genetic heterogeneity and cancer drug resistance . The lancet oncology . 2012 ; 13 ( 4 ): e178 - e85 . https://doi.org/10.1016/S1470-2045( 11 ) 70335 - 7 PMID: 22469128
11. Davnall F , Yip CS , Ljungqvist G , Selmi M , Ng F , Sanghera B , et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights into imaging . 2012 ; 3 ( 6 ): 573 - 89 . https://doi.org/ 10.1007/s13244-012 -0196-6 PMID: 23093486