Key words thorax - CT - CT-quantitative - adenocarcinoma - technical aspects
Introduction
Part-solid pulmonary nodules (PSN) are a frequent incidental finding in chest CTs,
for example in the setting of a lung cancer screening program. Persistent PSN have
been reported to be malignant more frequently than solid or pure ground glass nodules
(PGGN) with rates as high as 93.3 % [1 ]. In case of malignancy they usually represent adenocarcinoma and its precursors
[2 ]. On the other hand, they can also represent various benign entities like infection,
inflammation, focal interstitial fibrosis, eosinophilic pneumonia, thoracic endometriosis,
focal hemorrhage or organizing pneumonia and have been shown to be transient in up
to 69.8 % of cases [3 ]. This variety can make diagnosis challenging and dependent on the experience of
the radiologist who has to take into account multiple lesion features such as attenuation,
location inside the lung, size, shape and whether singular vs. multiple. But even
if one considers all these particularities, diagnosis is often not certain requiring
follow-up examinations for documentation of resolution, persistence or growth [5 ]. Moreover, because the size of many of these nodules is small, how size is measured
becomes especially important. Variations in CT-scanners, window settings, as well
as inter- and intra-rater performance are common and may have a critical impact on
the assessment of size, especially in the follow-up [6 ]. Presently, manual uni- or bidirectional diameter measurements are the standard
for lung cancer screening programs and day to day clinical care as reflected in current
guidelines regarding the management of pulmonary nodules [5 ]
[7 ]. In PSN both the diameter of the whole lesion and of the solid part should be measured
while the focus should be on the solid component, because although the solid component
does not always correlate with the pathologically determined invasive component, there
is a general correlation between them. [4 ]
[5 ]
[6 ]
[8 ].
There is accumulating evidence that semi-automated computer-aided volumetry (CAV)
has several advantages compared to manual diameter measurements. The Dutch-Belgian
lung cancer screening trial (NELSON), which was the first screening program to use
semi-automated CAV instead of manual diameter measurements, achieved high negative
predictive values and presumably fewer false-positive results compared to other lung
cancer screening trials [9 ]
[10 ]. Furthermore, the volume-based management protocol yielded high sensitivity and
specificity for the 2-year lung cancer probability [11 ]. Heuvelmans et al. [12 ] concluded in their investigation of diameter and volume measurements for estimation
of lung nodule size that the use of mean or maximum axial diameter to assess the size
of intermediate-sized lung nodules leads to a substantial overestimation of nodule
volume, compared with semi-automated volumetry and that median intra-nodular diameter
variation exceeds the 1.5 mm growth cut-off advocated in screening guidelines such
as LungRADS implying a significant potential for errors in nodule management. It is
not trivial to measure the accuracy of semi-automated volumetry since the “true” size
of any pulmonary nodule is in most cases unknown. The reference standard of volume
measurement after nodule excision is not perfect due to factors like an inevitable
bias toward larger nodules, differences in pathology handling techniques and variations
in the degree of lung inflation [13 ]. Nevertheless, several phantom studies have delivered promising results [14 ]
[15 ]
[16 ]
[17 ]. Apart from accurate estimation of the nodule volume, in clinical practice it is
arguably more important for the software to possess high levels of intra- and interrater
reliability since many nodules require follow-up examinations. In the past the variability
range of nodule volume has been reported to be in general approximately 25 % [18 ].
The objective of this study was to test the performance of a software prototype for
semi-automated computer-aided volumetry of part-solid pulmonary nodules with separate
segmentation of the whole lesion and the solid component and to compare results with
those acquired by manual volumetry.
Material and Methods
Study population
This retrospective evaluation of CT image data was approved by the local institutional
review board (registration number 187/2018BO2). A retrospective database search of
the local radiology department identified 34 chest CT scans of 19 consecutive patients
(median age 75 years; range, 55–91 years; 8 female) diagnosed with part-solid pulmonary
nodules (n = 66) in the routine CT-work-up between February 2015 and February 2018.
CT examinational protocol
All chest CTs were obtained unenhanced at end-inspiratory phase. In total, 34 CT-image
data sets with a mean of 2 (range, 1–10) follow-up examinations were evaluated. CT-examinations
were performed using a multi-detector scanner (SOMATOM Definition Flash, Siemens Healthineers,
Forchheim, Germany), a 300–350 mm field of view, a 512 × 512 reconstruction matrix,
120 kV, 100 effective mAs and a tube rotation time of 0.5 ms. In all patients a spiral
acquisition was obtained from the apex to the base of the lungs. Patients were positioned
supine with the arms stretched in elevation and abduction. Thin-slice CT scans (0.6 mm)
were reconstructed using a smooth reconstruction kernel (filter, B31f). For 54 lesions
both smooth and sharp kernel reconstructions (filter, B70f) were available. All chest
CTs were analysed for the existence of additional pathologies e. g. pleural effusions,
pulmonary oedema, haemorrhage or pneumonia, that could have impacted volumetry results
in which case they were excluded from the final analysis.
Functioning of the software prototype
Implementation of manual segmentation
The complete chest CT image dataset is displayed in three planes. The reader identifies
the PSN and uses a designated tool to manually draw the edges of the whole lesion
and of the solid part on every image the nodule is visible on. The edges can be drawn
and freely adjusted in all three planes. After finalizing the manual segmentation,
the software automatically calculates the volume and longest axial diameter of both
the entire nodule and the solid part without displaying results to the reader.
Implementation of semi-automated computer-aided segmentation
The complete chest CT image dataset is displayed in three planes. The reader identifies
the PSN and subjectively selects the axial slice in which the lesion shows the longest
diameter. A designated tool is used to draw a straight line (seed line) through the longest diameter. The software then immediately performs automatic segmentation
separately for the entire nodule and the solid part and calculates the volumes and
longest axial diameters. The reader is blinded to the segmentation results.
Technical description of semi-automated computer-aided segmentation
After initializing CAV via drawing, the seed line the algorithm then computes a histogram
of the attenuation of the voxels marked by the seed line to differentiate between
solid lesions (i. e. parenchymal consolidation obscuring pulmonary structures like
vessels and bronchi) and part-solid lesions. If the 25 % quantile of the histogram
exceeds a predefined attenuation threshold, a pure solid lesion is assumed. In this
case the lesion is segmented through region growing followed by morphological operations.
The algorithm determines whether the lesion shows direct pleural contact, in which
case the nodule is separated from the pleura. A detailed description of the algorithm
can be found in a study by Moltz et al. [19 ]. If the histogram analysis does not indicate a pure solid lesion (i. e. detects
the presence of ground-glass opacification which shows higher attenuation than normal
lung parenchyma and lower attenuation than the solid portion and pulmonary soft tissues
such as vessels or bronchi), the entire part-solid lesion is segmented through region
growing with boundaries determined via intensity analysis of the nodule region and
surrounding parenchyma [20 ]. This is followed by morphological operations analogous to the ones performed for
solid lesions. In part-solid lesions the denser structures belonging to the solid
compartment of the lesion are identified via thresholding: the center of the largest
solid structure is used as a seed point to segment a solid compartment with the same
algorithm as for pure solid lesions described above. The solid compartment is restricted
to the boundaries of the subsolid compartment. The algorithm accounts for partial
volume effects when determining the volumes of the solid and the subsolid compartment
[21 ]. The reported subsolid volume includes the volume of the solid compartment. Examples
of a segmentation results are given in [Fig. 1 ], [2 ].
Fig. 1 Sample images of a 78-year-old male patient with an adenocarcinoma in the right upper
lobe. Images demonstrate results of manual segmentation using the smooth reconstruction
kernel (b , c ) and semi-automated computer-aided segmentation using the smooth d and sharp kernel e .Abb. 1 Beispielbilder eines 78 Jahre alten männlichen Patienten mit einem Adenokarzinom
im rechten Oberlappen. Die Bilder zeigen die Ergebnisse der manuellen Segmentierung
mit dem weichen Rekonstruktionskernel (b , c ) und der halbautomatischen computergestützten Segmentierung mit dem weichen d und harten Kernel e .
Fig. 2 Sample images of a 76-year-old female patient with an adenocarcinoma in the right
upper lobe. Images show the results of semi-automated computer-assisted segmentation
using the smooth (left) and sharp reconstruction kernel (right).Abb. 2 Beispielbilder einer 76 Jahre alten Patientin mit einem Adenokarzinom im rechten
Oberlappen. Die Bilder zeigen die Ergebnisse der halbautomatischen computergestützten
Segmentierung mit dem weichen (links) und dem harten Rekonstruktionskernel (rechts).
Manual and computer-assisted segmentation and volumetry
For each of the 66 part-solid nodules 4 sets of volume measurements (MV1, MV2, CAV1,
CAV2) were produced by two radiology residents and two medical students, each set
containing separate measurements of the entire PSN and the solid part: Manual volumetry
performed by Radiologist 1 (MV1), manual volumetry performed by Radiologist 2 (MV2),
CAV performed by medical student 1 (CAV1) and CAV performed by medical student 2 (CAV2).
Radiologist 1 did not have any significant experience in reading chest CTs, Radiologist
2 had three years of experience.
In a subset of 54 part-solid nodules CT datasets had been reconstructed with both
the smooth and the sharp kernel. In this subset two additional sets of CAV measurements
(CAVsmooth, CAVsharp) were produced by medical student 1, each set again containing
separate measurements of the entire PSN and the solid part: CAV was performed three
seperate times in each PSN using different variants of seed lines according to the
following instructions: Seed line 1: “Draw a seed line through the longest diameter.”;
Seed line 2: “Draw a seed line through the longest diameter but be a little imprecise.”;
Seed line 3: “Draw a seed line through the approximate longest diameter and extend
the seed line into the surrounding lung parenchyma.” The reader was blinded to the
segmentation results. The average of the three volume measurements was calculated
(CAVsmooth). The manually-drawn seed lines were then transferred to the CT data sets
that had been reconstructed with the sharp kernel to obtain CAV results for both kernels
using the exact same seed lines and the average of the three volume measurements was
calculated (CAVsharp).
Analysis
Subjective visual assessment
Four weeks after the production the blinded segmentation results MV1, MV2 and CAV1
were shown to a senior radiologist with 25 years of experience in reading chest CTs
(Radiologist 3) and to Radiologist 2. They visually assessed the quality of the results
in the following manner: A dedicated software program was used. The readers selected
each of the segmented 66 PSNs from a list. The selected PSN is shown in the axial
CT images with the segmentation results displayed as colored lines surrounding the
edges of the entire nodule and the solid part, each color representing one of the
three datasets. The readers were able to select which of the separate segmentation
results were displayed at any time with the option to display any combination or no
result at all. This ensured that the lesion itself could be examined well and that
segmentation results could be compared directly. The readers visually evaluated the
segmentation results, i. e. how exact the lines depicted the borders of the solid
part and the entire nodule. Each single segmentation result was evaluated as either
satisfactory or unsatisfactory via consensus reading.
Quantitative statistical analysis
The following parameters were evaluated:
1. CAV Accuracy
CAV accuracy was assessed via comparing semi-automated CAV (CAV1 and CAV2) to the
calculated average of the two radiology residentsʼ manual volume measurements (MV1
and MV2), which was defined as the reference standard, using the Bland-Altman method
[22 ]
[23 ].
2. CAV and manual volumetry interrater variability
The interrater variability of CAV and manual volumetry was assessed by comparing the
results of semi-automated CAV performed by the two medical students (CAV1 and CAV2)
and the results of manual volumetry performed by Radiologist 1 and 2 (MV1 and MV2)
using the Bland-Altman method and calculating the intraclass correlation coefficient
(ICC).
3. CAV intra-rater variability
The intra-rater variability of CAV was assessed by determining each minimum and maximum
measurement out of the three separate measurements per PSN performed by medical student 1
in the CT datasets that had been reconstructed with the smooth kernel (CAVsmooth).
These were then compared via the Bland-Altman method. Additionally, we calculated
the ICC for the three separate measurements.
4. Variability between the smooth and sharp reconstruction kernel
Variability of CAV measurements between the smooth and the sharp reconstruction kernel
was assessed via comparing the calculated average values of CAVsmooth with those of
CAVsharp using the Bland-Altman method.
Bland-Altman analysis consists of calculation of the relative differences in volume
measurements, i. e. the difference in two measurements divided by the mean volume.
Volume measurement variability is defined as the 95 % confidence interval of these
relative differences. ICC estimates and their 95 % confidence intervals were calculated
based on a single rater, absolute-agreement, two-way random-effects model. A p-value
of 0.05 was considered statistically significant. We used the computer software IBM
SPSS Statistics 26 and GraphPad Prism 9.
Results
Mean values and standard deviations for volumes and diameters of the entire lesion
and the solid part are presented in [Table 1 ].
Table 1
Mean volumes [mm³] and longest axial diameters [mm] with standard deviations of the
entire PSN and the solid lesion part acquired by manual volumetry and CAV (± standard
deviation).Tab. 1 Durchschnittliches Volumen [mm3 ] und größter axialer Durchmesser [mm] mit Standardabweichungen der gesamten PSN und
der soliden Anteile, ermittelt mittels manueller Volumetrie und CAV (± Standardabweichung).
manual volumetry (reader 1)
manual volumetry (reader 2)
CAV (student 1)
volume entire PSN
1401 (± 2929)
1607 (± 3420)
1213.0 (± 2706)
volume solid part
272 (± 500)
245 (± 376)
266 (± 440)
diameter PSN
15.0 (± 7.2)
15.8 (± 8.4)
12.3 (± 8.3)
diameter solid part
9.1 (± 3.7)
9.2 (± 3.6)
8.7 (± 4.2)
Subjective visual assessment
Manual segmentation of the solid part was rated as satisfactory in 79 %–80 %. Manual
segmentation of the entire nodule was rated as satisfactory in 73 %–76 %. Semi-automated
computer-assisted segmentation delivered satisfactory results in 77 % for the solid
part and 67 % for the entire nodule ([Table 2 ]).
Table 2
Results of subjective visual assessment. Percentage of segmentation results rated
as satisfactory.Tab. 2 Ergebnisse der visuellen Bewertung. Prozentsatz der Segmentierungsergebnisse, die
als zufriedenstellend gewertet wurden.
solid part
Manual volumetry (Radiologist 1)
79 % (52/66)
Manual volumetry (Radiologist 2)
80 % (53/66)
CAV (Medical Student 1)
77 % (51/66)
entire PSN
Manual volumetry (Radiologist 1)
73 % (48/66)
Manual volumetry (Radiologist 2)
76 % (50/66)
CAV (Medical Student 1)
67 % (44/66)
Statistical analysis of volumetry
Numbers in brackets following ICC values indicate the lower and upper bounds of their
95 % confidence intervals.
1. CAV Accuracy
For the solid part relative variability between CAV1 / CAV2 and the reference standard
was –150–116 %/–151–117 % with a mean relative difference of –17 %/–17 %. For the
entire nodule relative variability was –106–54 %/–63–49 % with a mean relative difference
of –26 %/–7 %. The respective Bland-Altman plots are shown in [Fig. 3 ].
Fig. 3 CAV Accuracy. Bland-Altman plots depicting variability between CAV1 and CAV2 and
the reference standard. The mean differences (middle dotted line) and the upper and
lower 95 % limits of agreement (upper and lower dotted lines) were as follows (limits
of agreement in parenthesis): a : –17 (–150–116), b : –17 (–151–117), c : –26 (–106–54), d : –7 (–63–49).Abb. 3 CAV Accuracy. Bland-Altman-Plots zur Darstellung der Variabilität zwischen CAV1 und
CAV2 und dem Referenzstandard. Die Mittelwerte der Differenz (mittlere gestrichelte
Linie) und die oberen und unteren 95 % Limits of Agreement (obere und untere gestrichelte
Linien) waren wie folgt (Limits of Agreement in Klammern): a : –17 (–150–116), b : –17 (–151–117), c : –26 (–106–54), d : –7 (–63–49).
2. CAV interrater variability
For the solid part relative variability between CAV1 and CAV2 was –16–16 % with a
mean relative difference of –0.075 %. For the entire nodule relative variability was
–102–65 % with a mean relative difference of –18 %. The respective Bland-Altman plots
are shown in [Fig. 4 ]. Regarding the solid part the ICC was 0.998 (0.997, 0.999). For the entire lesion
the ICC was 0.880 (0.806, 0.926).
Fig. 4 Interrater variability. Bland-Altman plots depicting interrater variability between
CAV1 and CAV2 and between MV1 and MV2. The mean difference (middle dotted line) and
the upper and lower 95 % limits of agreement (upper and lower dotted lines) were as
follows (limits of agreement in parenthesis): a : –0.075 (–16–16), b : –18 (–102–65), c : –3.6 (–89–82), d : –5.9 (–46–34).Abb. 4 Interrater-Variabilität. Bland-Altman-Plots zur Darstellung der Interrater-Variabilität
zwischen CAV1 und CAV2 und zwischen MV1 und MV2. Die Mittelwerte der Differenz (mittlere
gestrichelte Linie) und die oberen und unteren 95 % Limits of Agreement (obere und
untere gestrichelte Linien) waren wie folgt (Limits of Agreement in Klammern): a : –0.075 (–16–16), b : –18 (–102–65), c : –3.6 (–89–82), d : –5.9 (–46–34).
3. CAV intra-rater variability
For the solid part relative intra-rater variability was –70–49 % with a mean relative
difference of –10 %. For the entire nodule variability was –111–31 % with a mean relative
difference of –40 %. The respective Bland-Altman plots are shown in [Fig. 5a, b ]. The ICC of the three separate measurements per PSN performed by medical student
1 was 0.992 (0.988, 0.995) for the solid part and 0.929 (0.883, 0.958) for the entire
nodule.
Fig. 5 CAV intra-rater variability; intrascan variability between reconstruction kernels.
Bland-Altman plots depicting intra-rater variability for CAVsmooth and the intrascan
variability between the smooth and sharp reconstruction kernels. The mean difference
(middle dotted line) and the upper and lower 95 % limits of agreement (upper and lower
dotted lines) were as follows (limits of agreement in parenthesis): a : –10 (–70–49), b : –40 (–111–31), c : –3.2 (–45–39), d : 13 (–21–46).Abb. 5 Intrarater-Variabilität. Bland-Altman-Plots zur Darstellung der Intrarater-Variabilität
für CAVsmooth und der Intrascan-Variabilität zwischen dem weichen und harten Rekonstruktionskernel.
Die Mittelwerte der Differenz (mittlere gestrichelte Linie) und die oberen und unteren
95 % Limits of Agreement (obere und untere gestrichelte Linien) waren wie folgt (Limits
of Agreement in Klammern): a : –10 (–70–49), b : –40 (–111–31), c : –3.2 (–45–39), d : 13 (–21–46)
4. Variability between the smooth and sharp reconstruction kernel
For the solid part relative variability of CAV measurements between the smooth and
the sharp reconstruction kernel was –45–39 % with a mean relative difference of –3.2 %.
For the entire nodule variability was –21–46 % with a mean relative difference of
13 %. The respective Bland-Altman plots are shown in [Fig. 5c, d ].
Discussion
Overall the software prototype showed mixed results. Subjective assessment of CAV
yielded satisfactory results with a somewhat higher rate of satisfactory segmentation
results for the solid part. On the other hand, Bland-Altman analysis showed comparatively
lower accuracy and interestingly better results for the entire nodule compared to
the solid part. Since both, the subjective assessment of results as well as the establishment
of the reference standard were based on subjective visual delineation of the solid
and subsolid part’s edges, this could be a result of relatively high intra- and interrater
variability regarding this task. The reduced difference in attenuation between the
ground-glass component of a subsolid nodule and the surrounding lung parenchyma is
a known segmentation problem [13 ]. The subjective impression of the authors is that when performing manual segmentation,
the edges of the solid lesion parts often can be more easily and confidently identified
than those of the ground glass part because they are more sharply delineated. The
volumes measured by CAV were lower compared to the manually derived reference standard.
In clinical practice, rather than measuring the true size of a PSN – which is not
known – it is more important to detect size changes during follow-up, which requires
high intra- and interrater reliability. Bland-Altman analysis showed low interrater
variability for the solid part but relatively high variability for the entire nodule.
Expressed as ICCs the agreement was high for both. Interestingly, the interrater variability
of manual segmentation was lower for the entire nodule compared to the solid part.
Intra-rater variability of CAV was relatively high overall with lower values for the
solid part compared to the entire nodule. Expressed as ICCs the agreement was high.
Regarding differences between the two reconstruction kernels we found that with the
smooth kernel the volume of the solid part was measured slightly lower and the volume
of the entire nodule somewhat higher. Overall, variability between the kernels was
higher for the solid part compared to the entire nodule.
These findings are important because accurate and especially precise size measurement
of PSNs, a task that can be difficult to accomplish adequately when performed manually,
is vital for the estimation of their malignant potential in the initial assessment
and in a follow-up scenario. Additionally, valid quantification is particularly important
for the solid part of malignant nodules due to its known general correlation to the
invasive component [6 ].
There are not many publications examining semi-automated volumetry of part-solid nodules.
Most publications examine subsolid nodules in general, of which part-solid nodules
are a subset. Moreover, the studies including part-solid nodules did not for the most
part perform separate segmentation for the solid part.
In regard to the subjective evaluation of the segmentations’ quality, Benzakoun et
al. [24 ] examined 47 PGGNs and 50 PSNs and found satisfactory results in 81 %. Charbonnier
et al. [25 ] found satisfactory results in 80.6 % for the solid parts of 170 subsolid nodules.
These values are slightly better, but similar to ours. Intra-rater variability for
the entire nodule in other studies was lower than our own. Kim et al. [26 ] analyzed 72 PGGNs and 22 PSNs and found a variability of –7.6 % to 8.5 %. Park et
al. [27 ] examined 30 PGGNs and found a maximum variability of –9.1 % to 10.1 % with a sharp
reconstruction kernel and of –11.6 % to 11.8 % with a medium sharp reconstruction
kernel. Higher variability in our study might be a result of the deliberate manipulation
of the seed lines in repeated measurements and using a smooth kernel. Expressed as
an ICC Scholten et al. [29 ] found an agreement of 0.92 which almost equals our own results. Regarding interrater
variability for the entire lesion Kim et al. [26 ] found a variability of –11.7 % to 18.1 % and Park et al. [27 ] of –15.8 % to 13.4 % with a sharp reconstruction kernel and –11.1 % to 6.2 % with
a the medium sharp kernel. Those values also are lower than our own. However, expressed
as ICCs we found comparatively lower values as for example Scholten et al. [28 ]
[29 ] or Kamiya et al. [30 ]. In those two studies which included 24 PGGNs and 20 PSNs and 19 PGGNs and 14 PSNs
respectively, Scholten et al. found ICCs between 0.920 and 0.957. Kamiya et al. found
an ICC of 0.940 in an analysis of 4 PGGNs and 92 PSNs. Expressed as relative volume
deviation other authors found values between –1.2 % and 18.1 % [26 ]
[27 ]. With respect to volume measurements of the solid nodule part, Kamiya et al. in
the study cited above, found ICCs between 0.994–0.996, which are similar to our own
results.
Regarding differences in volume between manual and semi-automated measurements the
study by Scholten et al. demonstrated that the average volume was 24.3 %−26.5 % smaller
when measured manually [29 ]. This stands in contrast to our results which showed the reverse.
Our study is limited by its retrospective design with typical drawbacks such as the
fact that sharp kernel reconstructions were not available for all nodules. The number
of nodules is rather low in absolute terms but similar to other studies on this topic.
We did not have a histological gold standard to determine the accuracy of volume and
diameter measurements, but this is a common problem concerning publications on this
issue.
In conclusion, although the software prototype delivers satisfactory results when
segmentation is evaluated subjectively, quantitative statistical analysis revealed
room for improvement especially regarding the segmentation accuracy of the solid part
and the reproducibility of measurements of the nodule’s subsolid margins.
Accurate and reliable size measurement plays an important role in the management of
PSNs, which possess relatively high malignant potential
The workload regarding PSN management is going to increase with the implementation
of lung cancer screening programs
CAV has the potential to make nodule size quantification easier and faster if the
software’s accuracy and especially the reproducibility can reach the level of manual
size measurement or even surpass it
