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ORIGINAL RESEARCH n Thoracic imaging
884 radiology.rsna.org n Radiology: Volume 264: Number 3—September 2012
Solitary Pulmonary Nodular
Lung Adenocarcinoma: Correlation
of Histopathologic Scoring and Patient
Survival with Imaging Biomarkers1
Ho Yun Lee, MD
Ji Yun Jeong, MD
Kyung Soo Lee, MD
Hyo Jin Kim, MD
Joungho Han, MD
Byung-Tae Kim, MD
Jhingook Kim, MD
Young Mog Shim, MD
Jae-Hun Kim, PhD
Inyoung Song, MD
Purpose: To evaluate the usefulness of histopathologic scoring for
survival prediction in patients with solitary pulmonary
nodular (SPN) lung adenocarcinomas and to correlate
the histopathologic scoring with the results of computed
tomography (CT) and fluorine 18 fluorodeoxyglucose pos-
itron emission tomography (PET)/CT.
Materials and
Methods:
This retrospective study was institutional review board
approved and the requirement for informed consent
was waived. A total of 148 patients with SPN lung ad-
enocarcinoma underwent PET/CT and CT. Correlations
between histopathologic scores estimated by using two
predominant histologic subtypes from each surgically re-
sected specimen and the mass of the nodule at CT or
maximum standardized uptake value (SUVmax) at PET/CT
were assessed. Disease-free survival (DFS) was estimated
by using the Kaplan-Meier method, and the log-rank test
was used to evaluate differences in each histopathologic
subtype.
Results: In 135 (91%) patients, tumors had a mixed subtype. The
most frequently observed histologic subtypes, in decreas-
ing order, were acinar (51%), lepidic (18%), solid (10%),
and papillary (9%). DFS rates at 5 years were higher than
90% for the group of patients with nodules that showed
the lepidic growth pattern, and 50% for patients with
nodules that showed the micropapillary pattern. The
pathologic score proved to be a significant predictor of
DFS (P , .001). Both SUVmax and the mass of the nodule
were closely correlated with pathologic score.
Conclusion: Pathologic scoring appears to help predict DFS in patients
with SPN lung adenocarcinoma and shows close correla-
tion with imaging biomarkers including the mass of the
nodule at CT and SUVmax at PET/CT.
q RSNA, 2012
1 From the Department of Radiology and Center for Imaging
Science (H.Y.L., K.S.L., H.J.K., J.H.K., I.S.), and Departments
of Pathology (J.Y.J., J.H.), Nuclear Medicine (B.T.K.), and
Department of Thoracic Surgery (J.K., Y.M.S.), Samsung
Medical Center, Sungkyunkwan University School of Medi-
cine, 50 Ilwon-Dong, Kangnam-Ku, Seoul 135-710, Korea.
Received August 23, 2011; revision requested October 13;
revision received November 18; accepted February 23,
2012; final version accepted March 26. Address corre-
spondence to K.S.L. (e-mail: kyungs.lee@samsung.com).
q RSNA, 2012
Note: This copy is for your personal non-commercial use only. To order presentation-ready
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Radiology: Volume 264: Number 3—September 2012 n radiology.rsna.org 885
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
Materials and Methods
Our institutional review board ap-
proved this retrospective study with a
waiver of informed consent. However,
CT and PET studies were performed
with patients’ informed consent for
safety issues.
Patients
The study group included 148 consecu-
tive patients with SPN adenocarcinoma
who were treated with surgery alone
or surgery and postoperative adjuvant
therapy at Samsung Medical Center
(Seoul, Korea) between July 2003 and
December 2007. Patients were identi-
fied in a search of the lung cancer surgi-
cal registry database of the department
of thoracic surgery. Inclusion criteria
for our study were as follows: (a) SPN
adenocarcinomas (3 cm or less in di-
ameter at CT) of clinical stage 1A or 1B
with no evidence of malignant satellite
nodules (proved with imaging study or
lung biopsy beforehand) and no hilar
or mediastinal lymphadenopathy on
imaging study or at mediastinoscopy,
(b) first treatment with surgery alone,
with or without postoperative adju-
vant treatment, (c) no other malignant
tumor history for 5 years before the
Consequently, in this new classification
system, common subtypes include
lepidic-growth predominant, acinar
predominant, papillary predominant,
micropapillary predominant, and solid
pattern predominant.
However, the problem of histologic
subtyping, subsequent tumor scoring or
grading, and prognosis prediction for
lung adenocarcinomas is that subtyping
is estimated mainly by using a resected
surgical specimen (whole tumor) post-
operatively, not by using core biopsy or
cytologic material preoperatively (7,9).
Therefore, preoperative prognostic pre-
diction by using a preoperative biopsy
pathologic specimen in small lung ad-
enocarcinomas seems incomplete, if it
is even possible. Thus, it is desirable
to predict patient prognosis by using a
preoperative surrogate biomarker with
imaging tools that can be substituted
for a histopathologic scoring system.
In the study by Lee et al (11), the
pathologic non-BAC ratio proved to
be the only independent risk factor
for poor prognosis in patients with a
solitary pulmonary nodular (SPN) lung
adenocarcinoma. However, in the Lee
et al study, the researchers evaluated
only the extent of BAC versus non-BAC
components. They did not analyze the
tumor histologic subtypes. Therefore,
the purpose of this study was to eval-
uate the usefulness of histopathologic
scoring for survival prediction in pa-
tients with SPN lung adenocarcinoma
and to correlate the histopathologic
scoring with the results of computed to-
mography (CT) and fluorine 18 fluoro-
deoxyglucose (FDG) positron emission
tomography (PET)/CT.
The 5-year overall survival rate for
patients with stage 1 lung cancer
approaches 67%, and 30%–40%
of these patients have a recurrence of
the disease at a later date (1–3). This
indicates the need to identify robust
prognostic biomarkers to help predict
which patients with early-stage cancer
are at the highest risk for recurrent
disease and, therefore, are candidates
for more aggressive surveillance or ad-
juvant therapy.
There is increasing evidence that
the characteristic histologic heteroge-
neity of lung adenocarcinomas means
that there is also diversity in progno-
ses among individual tumors. (4–6).
For example, mixed subtype tumors
that have micropapillary or solid pat-
terns are associated with worse out-
comes than other subtypes (7–9). In
2011, the International Association for
the Study of Lung Cancer, the Ameri-
can Thoracic Society, and the Euro-
pean Respiratory Society proposed
a new international multidisciplinary
classification system for lung adeno-
carcinoma (10). In this system, lesions
that were formerly considered to be
bronchioloalveolar carcinomas (BAC)
are now classified as preinvasive lesions
because patients with these 2–3-cm le-
sions have a 100% disease-free survival
(DFS) rate. In addition, because most
invasive lung adenocarcinomas consist
of a mixture of histologic subtypes and
the word “predominant” was appended
to all categories of invasive adenocarci-
nomas, the classification of “adenocar-
cinoma, mixed subtype,” was removed.
Implications for Patient Care
nImaging biomarker study results
are closely correlated with path-
ologic score and thus enable pre-
diction of patient survival.
nPreoperative evaluation of tumor
grade by using PET/CT or CT
may allow the selection of further
staging workup and appropriate
therapeutic strategies for
patients with small lung
adenocarcinomas.
Advances in Knowledge
nOf 148 tumors, 135 (91%) were
composed of mixed patterns, and
the most predominant subtypes
were, in a decreasing order,
acinar (51%), lepidic (18%),
solid (10%), and papillary (9%)
patterns.
nA pathologic scoring system
appeared to help predict patient
survival and had close correlation
with both SUVmax at PET/CT (P
, .001) and mass of the nodule
at CT (P = .004).
Published online before print
10.1148/radiol.12111793 Content codes:
Radiology 2012; 264:884–893
Abbreviations:
AIS = adenocarcinoma in situ
BAC = bronchioloalveolar carcinoma
DFS = disease-free survival
FDG = fluorine 18 fluorodeoxyglucose
SPN = solitary pulmonary nodular
SUVmax = maximum standardized uptake value
Author contributions:
Guarantor of integrity of entire study, K.S.L.; study
concepts/study design or data acquisition or data
analysis/interpretation, all authors; manuscript drafting or
manuscript revision for important intellectual content, all
authors; approval of final version of submitted manuscript,
all authors; literature research, H.Y.L., J.Y.J., K.S.L.; clinical
studies, H.Y.L., K.S.L., H.J.K., J.H., B.T.K., J.K., Y.M.S.,
J.H.K., I.S.; experimental studies, J.Y.J.; statistical analysis,
H.Y.L., K.S.L.; and manuscript editing, H.Y.L., J.Y.J., K.S.L.,
Y.M.S.
Potential conflicts of interest are listed at the end
of this article.
886 radiology.rsna.org n Radiology: Volume 264: Number 3—September 2012
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
calcification and were scanned with-
out contrast medium injection, the
conversion process was feasible in
all cases. The mass of the nodule (in
grams) was calculated by multiplying
nodule volume (in cubic centimeters)
by mean nodule density.
Pathologic Evaluation
Each resected specimen (entire tu-
mor) was evaluated with standard
pathologic methods as described in
the surgical pathologic dissection
manual of the Department of Pathol-
ogy (17). All resected specimens were
designated R0 (no residual tumor at
the primary tumor site after surgi-
cal resection). Two experienced lung
pathologists (J.Y.J. and J.H., with 5
and 18 years of experience in lung
pathology, respectively) jointly inter-
preted all tissue sections. Tumor tis-
sue samples of approximately 10 mm
in diameter were obtained and each
was placed on a slide.
First, for each case, comprehen-
sive histologic subtyping was per-
formed for the primary tumor in a
semiquantitative manner. The extent
of existent tumor histologic subtypes
and central fibrosis was quantified to
the nearest 5% level, adding up to a
total of 100% subtype components
per tumor as described in Figure 1
(10). As previously reported (11,18),
the central fibrosis region was de-
fined as the areas of fibroblastic fo-
cus in which a moderate or abundant
amount of collagen or hyalinized tis-
sue was clearly noted. The region
did not contain any adenocarcinoma
in situ (AIS) components. Next, the
most predominant and second most
predominant patterns in a mixed-type
adenocarcinoma were defined as the
histologic subtypes that comprised
the highest and second highest per-
centage of the tumor. When evaluat-
ing the predominant pattern, the cen-
tral fibrosis area and its extent were
disregarded.
Next, tumors were graded by using
a three-tier grading system (9) (Fig 2).
Grade 1 included histologic subtypes
of AIS, minimally invasive adenocarci-
noma, and the lepidic pattern of invasive
a region of interest was drawn in a
presumed nodular location, taking
into account the CT component im-
ages of PET/CT. FDG uptake in the
regions of interest was analyzed by
using the maximum standardized up-
take value (SUVmax).
Chest CT data were sent directly
to a picture archiving and communi-
cation system (Path-Speed or Centric-
ity 2.0; GE Healthcare, Mt. Prospect,
Ill), which displayed all image data on
two monitors (1536 3 2048 matrix,
eight-bit viewable grayscale, 60–foot-
lambert [205.6 candela per square
meter] luminescence). The monitors
were used to view both mediastinal
(width, 400 HU; level, 20 HU) and
lung (width, 1500 HU; level, 2700
HU) window images.
The time between CT study and
surgical tumor removal ranged from
1 to 28 days (mean, 17.5 days; me-
dian, 13.5 days). CT images were
assessed retrospectively for nodule
size, appearance (pure ground-glass
opacity, part solid, and solid), vol-
ume, and mass of the nodule, inde-
pendently by one chest radiologist
(I.S., with 2 years of experience in
thoracic CT interpretation) and one
radiologic physicist (J.H.K., with
3 years of experience in radiolog-
ical physics), who were unaware of
the clinical PET findings and histo-
logic diagnoses. For nodule size, the
researchers measured the longest
tumor diameter on the transverse
lung window image where the larg-
est nodule dimension appeared. For
nodule volumetry, they delineated
nodule outlines electronically on all
transverse images on which any por-
tion of the nodule appeared. Then,
the computer automatically calcu-
lated the nodule volume by multiply-
ing the number of voxels by the unit
volume of a voxel (15). Physical den-
sity (in grams per cubic centimeter)
is roughly linear with CT attenuation
(Hounsfield units) (16). From this lin-
ear relationship, the physical density
of the nodule could be extrapolated
from the mean CT attenuation mea-
surements of the nodule. Because the
nodules in our study did not contain
diagnosis of lung adenocarcinoma, (d)
both integrated FDG PET/CT and chest
CT studies acquired within 1 month be-
fore resection, (e) patient alive 30 days
after surgery, and (f) no loss of patient
to follow-up during the 12 months after
surgery.
The cases were reviewed according
to International Multidisciplinary Lung
Adenocarcinoma Classification criteria
(10) and staged according to the sev-
enth edition of the TNM classification
for lung cancer (12,13).
Imaging and Interpretation
Imaging characteristics of each pri-
mary lung tumor were evaluated by
using chest CT and the PET compo-
nent images of PET/CT. PET/CT and
chest CT were performed within a
2-week period (average time interval,
7 days; range, 0–14 days). FDG PET/
CT images were acquired by using
a PET/CT device (Discovery LS; GE
Healthcare, Milwaukee, Wis), which
consisted of a PET scanner (Advance
NXi; GE Healthcare) and an eight-
section CT scanner (Light-Speed Plus;
GE Healthcare). The imaging methods
were described in detail in a previous
report (14). Helical CT images were
obtained with an eight– (LightSpeed
Ultra, GE Healthcare) or 16–detector
row (LightSpeed16, GE Healthcare)
CT scanner. Unenhanced CT images
were obtained with the following pa-
rameters: detector collimation, 0.625
mm; field of view, 34.5 cm; beam
pitch, 1.35 or 1.375; gantry speed,
0.6 second per rotation; 120 kVp;
150–200 mA; and section thickness,
1.25 mm for transverse images. All
imaging data were reconstructed by
using soft-tissue algorithms.
A nuclear medicine physician
(B.T.K., with 13 years of experience
in PET/CT interpretation) who was
unaware of clinical and pathologic re-
sults evaluated the PET images. For a
semiquantitative analysis of FDG up-
take, regions of interest were placed
over the most intense areas of FDG
accumulation. In some patients, nod-
ular FDG uptake could not be identi-
fied on the PET component images of
their PET/CT study. In those patients,
Radiology: Volume 264: Number 3—September 2012 n radiology.rsna.org 887
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
Figure 1
Figure 1: Photomicrographs show morphology of invasive adenocarcinoma subtypes, including (a) lepidic
pattern, (b) acinar pattern, (c) papillary pattern, (d) micropapillary pattern, and (e) solid pattern (hematoxylin-
eosin stain; magnification ×200).
histologic type, the primary grade was
used, and the score was determined by
doubling the primary tumor grade.
Treatment and Follow-up Evaluation
For stage 1 cancers, all 127 patients had
undergone either sublobar resections
(wedge resections or segmentectomies)
or lobectomies. None of the patients
with stage 1 cancer underwent adju-
vant therapy. Of the patients with stage
2 and 3A cancers, 17 had undergone
sublobar resections and the remaining
four had undergone either pneumonec-
tomy or lobectomy. These 21 patients
were treated with adjuvant concurrent
combined chemotherapy and radiation
adenocarcinoma in accordance with the
2011 international lung adenocarcinoma
classification system. Grade 2 corre-
sponded to tumors that showed acinar
or papillary patterns. Grade 3 corre-
sponded to the tumors that showed mi-
cropapillary or solid patterns (9). When
tumors were composed predominantly
of a variant pattern, the tumors were
removed from our study because they
were uncommon and heterogeneous in
terms of biologic behavior and prognosis
(10).
Finally, pathologic tumor scores were
calculated by adding the two most predom-
inant grades in each tumor (9) (Table 1).
When tumors were composed of a pure
therapy (five cycles of cisplatin chemo-
therapy [one cycle: an intravenous in-
jection of cisplatin, 25 mg/m2, on day
1 for 1 week] and 25 Gy of radiation in
1st week). Median follow-up time after
surgery for all patients was 50 months
(range, 36–92 months). Sixty-nine per-
cent (102 of 148) of patients had a fol-
low-up time of less than 5 years. By July
2011, 32 (22%) patients developed re-
current disease after surgical resection,
and the median time to recurrence was
39 months (range, 10–76 months). Of
those patients, 19 had pulmonary me-
tastasis. Metastases to mediastinal
lymph nodes, pleura, brain, liver, or
bone were detected in other patients.
None had local tumor recurrences.
Statistical Analysis
For measuring tumor size, volume, and
mass of the nodule, the means of values
measured by two observers were record-
ed, and interobserver variability was
calculated by using repeated measures
data analysis for the intraclass correla-
tion coefficient. DFS was defined as the
time from surgery to recurrence, lung
cancer–related death, or last follow-up
evaluation. DFS was estimated by using
the Kaplan-Meier method. The log-rank
test was used to evaluate the statistical
significance of differences in survival
among patient groups with different
scores. Cause of death was determined
from death certificates or through corre-
spondence with the physician in charge.
888 radiology.rsna.org n Radiology: Volume 264: Number 3—September 2012
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
using the Spearman correlation coeffi-
cient test. Differences in statistical signif-
icance among subcategorized groups that
were contingent on pathologic scores
were compared in terms of the mass of
the nodules at CT and FDG uptake at
PET by using the Kruskal-Wallis test.
Statistical analyses were performed by
Causes of death other than recurrence
of the tumor were censored from the
survival analysis.
Interrelationships among the mass of
the nodule at CT, FDG uptake at PET,
and the pathologic score determined on
the basis of the two most predominant
histologic subtypes were assessed by
Figure 2
Figure 2: Lung adenocarcinoma in a 67-year-old woman. (a) Targeted view of transverse lung window
CT image shows 25-mm solid nodule with peripheral ground-glass opacity (arrow). (b) On mediastinal
window image, solid area remains, but ground-glass opacity area is not visible. Mass of nodule was
estimated as 13.3 g. (c) PET/CT image shows FDG uptake with SUVmax of 7.8. (d) Photomicrograph shows
internal scar tissue (*), surrounding areas of acinar (**) and solid (#) adenocarcinoma patterns, and lepidic
pattern (arrows) showing uniform cuboid cellular proliferation along alveolar walls only at tumor periph-
ery. (Hematoxylin-eosin stain; original magnification, 310.) (e) Schematic of tumor components shows
estimated percentages of grade 1 (yellow area, 10%), grade 2 (blue area, 50%), grade 3 (green area, 30%),
and central fibrosis (red area, 10%).
using software (SPSS, version 19.0, 2010;
SPSS, Chicago, Ill). A P value less than
.05 was considered to indicate a statisti-
cally significant difference.
Results
CT Findings and Patient Demographics
Of the 148 nodules, 10 showed ground-
glass opacity, 131 were part solid, and
seven were solid nodules. Tumors were
22 mm (range, 7–30 mm) in mean diam-
eter. Their sizes were less than or equal
to 10 mm in diameter in 15 patients
and greater than 10 mm in diameter in
133 patients. Interobserver agreement
for measurements of the volume and
mass of the nodules were moderate, and
agreement for size was high. Intraclass
correlation coefficients were 0.71 (95%
confidence interval [CI]: 0.67–0.75) for
volume, 0.69 (95% CI: 0.63–0.75) for
mass, and 0.88 (95% CI: 0.85–0.91) for
the size of nodules. The clinicopathologic
characteristics of the 148 patients with
lung adenocarcinomas included in this
study are summarized in Tables 2 and 3.
Histopathologic Characteristics and
Association with Outcome
Among 148 patients, 95 (64%) had nod-
ules that showed the acinar pattern,
Radiology: Volume 264: Number 3—September 2012 n radiology.rsna.org 889
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
new histologic subtype. Variants include
invasive mucinous adenocarcinoma
(formerly mucinous BAC), colloid, fe-
tal, and enteric adenocarcinoma.
Our study results included three
main findings: (a) The new Interna-
tional Multidisciplinary Lung Adeno-
carcinoma Classification scheme, along
with our modifications, which reflect
the quality and quantity of the patho-
logic pattern, can help in the prediction
SUVmax (P , .001) and the mass of the
nodule (P , .004) were found among
tumors of different pathologic scores.
Moreover, post hoc Bonferroni com-
parisons showed all cross-score parings
to be statistically distinct with regard
to SUVmax and the mass of the nodule,
except the paring of pathologic scores 2
and 3. SUVmax was strongly correlated
with pathologic score. The mass of the
nodule was also strongly correlated
with pathologic score (Fig 4).
Discussion
Lung adenocarcinoma is highly het-
erogeneous, and it usually has variable
combinations of two or more histo-
logic subtype patterns (19). In the in-
ternational criteria proposal (10), new
concepts were introduced, such as the
classification of small solitary adenocar-
cinomas with pure lepidic growth as AIS
and those with predominantly lepidic
growth but with invasion of 5 mm or
less as minimally invasive adenocarci-
noma. Invasive adenocarcinomas were
classified by the predominant pattern
after using comprehensive histologic
subtyping with lepidic (formerly most
mixed subtype tumors with nonmuci-
nous BAC), acinar, papillary, and solid
patterns; micropapillary was added as a
27 (18%) showed the lepidic pattern, and
eight (5%) were AIS. One-hundred thir-
ty-five (91%) of the tumors were mixed-
subtype adenocarcinomas. The most
frequently observed histologic subtypes
(considering the two most predominant
subtypes we observed in each tumor)
were acinar (51%), lepidic (18%), solid
(10%), and papillary (9%). DFS at 5
years was higher than 90% for the group
of patients with AIS, minimally inva-
sive adenocarcinoma, and nodules that
showed the lepidic pattern, and 50% for
nodules that showed the micropapillary
pattern (Table 4).
DFS curves for patient groups ac-
cording to pathologic score are shown
in Figure 3. Pathologic scores, which
were computed as the sum of the tumor
grades of the two most predominant
subtypes, were found to be significant
predictors of DFS (P , .001).
Correlation among Radiologic, Metabolic,
and Histopathologic Factors
In Tables 4 and 5 and in Figure 4, the
interrelationship between the mass of
the nodule at CT, SUVmax at PET, and
pathologic score are expressed. SPN
adenocarcinomas with a high pathologic
score were shown to have higher mass
and higher FDG uptake than those with
a low score. A significant difference in
Table 2
Patient Characteristics
Characteristic No. of Patients
Sex
Male 66 (45)
Female 82 (55)
Median age (y) 59 (37–80)*
Smoking
Nonsmoker 86 (58)
Current or former smoker 62 (42)
Lymph node metastases
Mediastinal 7 (5)
Hilar 11 (7)
No lymph nodes 130 (88)
Subtype predominance†
AIS 16 (5)
MIA 10 (3)
Lepidic 53 (18)
Acinar 152 (51)
Papillary 25 (9)
Solid 28 (10)
Micropapillary 12 (4)
Pathologic score†
2 13 (9)
3 26 (18)
4 80 (54)
5 24 (16)
6 5 (3)
Median follow-up period (mo) 50 (36–92)*
Recurrence
Yes 32 (22)
No 116 (78)
DFS at 5 years 0.82§
Note.—Unless otherwise indicated, data in parentheses
are percentages. MIA = minimally invasive adeno-
carcinoma.
* Data in parentheses are the range.
† Data include the two most predominant subtypes in
each lesion (n = 296).
‡ Score is the sum of the two most predominant tumor
grades in each lesion (9).
§ Number is proportion of patients with DFS at 5 years.
Table 1
Pathologic Scoring of Lung Adenocarcinomas
Score
Grades of Two Most
Predominant Subtypes* Representative Pattern
2 1 and 1 AIS, minimally invasive adenocarcinoma
3 1 and 2 or 2 and 1 Mixed subtype, well differentiated with lepidic and
acinar or papillary patterns
4 2 and 2, 1 and 3, or 3 and 1 Mixed subtype, moderately differentiated with acinar
and/or papillary patterns, or lepidic with solid or
micropapillary patterns, pure acinar or pure papillary
5 2 and 3 or 3 and 2 Mixed subtype, poorly differentiated with acinar or papillary
and micropapillary or solid patterns
6 3 and 3 Mixed subtype, poorly differentiated with predominant
solid and micropapillary patterns, pure solid, or pure
micropapillary patterns
Note.—Tumors were graded according to the International Multidisciplinary Lung Adenocarcinoma Classification and scored on
the basis of the two most predominant subtypes (9).
* Grade 1 = AIS or minimally invasive adenocarcinoma and lepidic pattern in mixed tumors; grade 2 = acinar and papillary
patterns; grade 3 = micropapillary and solid patterns (10).
890 radiology.rsna.org n Radiology: Volume 264: Number 3—September 2012
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
Table 3
Pathologic Stage versus Predominant Histologic Subtype
Predominant Subtype Stage 1A Stage 1B Stage 2A Stage 2B Stage 3A Total
AIS 8 0 0 0 0 8
MIA 5 0 0 0 0 5
Lepidic 25 2 0 0 0 27
Acinar 51 23 4 10 7 95
Papillary 3 4 0 0 0 7
Solid 3 1 0 0 0 4
Micropapillary 0 2 0 0 0 2
Total 95 32 4 10 7 148
Note.—Data are number of patients. Lesions were staged according to the 7th revision of the TNM classification. MIA =
minimally invasive adenocarcinoma.
of tumor malignancy grade and patient
survival. (b) Both the SUVmax on PET
images and the mass of the nodule on
the CT images are closely correlated
with the pathologic score; and (c) both
SUVmax and the mass of the nodule at
CT could help to better stratify the
prognoses of patients, particularly for
those with higher grade tumors.
Our results may have practical
implications. With the help of preop-
erative imaging biomarker surrogate
study results, we were able to deter-
mine which patients with small lung
adenocarcinomas may develop recur-
rent disease after the initial treatment
and which ones may not, even without
examining a surgical tumor specimen.
The histologic subtyping and scoring of
SPN lung adenocarcinoma appear to
correlate with the mass of the nodule
at CT or the SUVmax at PET. In addi-
tion, the mass of the nodule or SUVmax
can be linked to the biologic aggressive-
ness of the tumor, and eventually to the
surgical outcome and patient survival
(11,20–23).
The tumor grading system has been
developed to correlate histopathologic
characteristics of SPN lung adenocar-
cinomas with prognostic importance,
and subsequently to compare the re-
sults with overall survival (7,9). In a
study by Barletta et al (7), the percent-
age of solid patterns as a reflection of
tumor architecture, the degree of cy-
tologic atypia, and the mitotic count
were evaluated to seek a prognostically
relevant grading system for lung adeno-
carcinomas. In the Barletta et al study,
a grading system that incorporated
the percentage of solid pattern and
the degree of cytologic atypia, which
was computed as the sum of the ar-
chitecture score and cytologic atypia,
appeared to be an independent predic-
tor of survival. Moreover, there was a
direct correlation between the degree
of cytologic atypia and the amount of
solid pattern. However, overall survival
was not associated with mitotic count.
In their study, the predominant histo-
logic pattern versus the patient progno-
sis was not assessed because of a lack
of tumors with dominant BAC (or AIS)
or micropapillary patterns in their co-
hort. In another study, Sica et al (9)
developed several separate scoring
systems, including (a) the sum of the
two most predominant grades (grade 1
= BAC or AIS, grade 2 = acinar and
papillary patterns, and grade 3 = solid
and micropapillary patterns) (b) the
sum of the two highest grades, and
(c) the sum of the predominant and
the highest grade. They tried to evalu-
ate both the quantity and combination
of specific patterns of histopathologic
subtypes for tumor metastatic poten-
tial. A scoring system based on the
two most predominant grades was the
best for categorizing patients as at low
or high risk for recurrence of disease
or death. In our study, a pathologic
scoring system reflecting the two most
Table 4
Imaging and Prognostic Features
Predominant Subtype No. of Patients Size (cm)* Volume (cm3)* Mass (g)* SUVmax* Follow-up Period (mo)* Recurrence†DFS at 5 Years‡
AIS 8 1.3 6 0.7 2.2 6 1.0 3.6 6 2.1 0 58.9 6 15.2 0 1
MIA 5 1.3 6 0.6 2.0 6 1.7 3.2 6 1.8 1.2 6 1.3 56.4 6 15.5 0 1
Lepidic 27 2.0 6 0.9 3.6 6 0.8 5.4 6 1.3 2.3 6 2.3 57.2 6 14.3 3 (11) 0.93
Acinar 95 2.3 6 0.8 5.9 6 1.1 11.5 6 2.1 5.4 6 3.6 52.1 6 11.7 24 (25) 0.78
Papillary 7 1.7 6 0.6 4.1 6 1.0 8.1 6 1.5 3.9 6 1.1 54.1 6 17.6 2 (29) 0.71
Solid 4 2.8 6 0.3 6.8 6 2.1 14.4 6 4.5 11.0 6 6.6 58.2 6 13.5 2 (50) 0.75
Micropapillary 2 2.6 6 0.6 4.7 6 1.5 10.6 6 2.0 4.7 6 3.1 64.3 6 9.7 1 (50) 0.50
Total 148 2.2 6 0.8 5.0 6 1.2 9.5 6 3.0 4.7 6 3.7 49.7 6 15.1 32 0.82
Note.—Classified According to the International Multidisciplinary Lung Adenocarcinoma Classification system. MIA = minimally invasive adenocarcinoma.
* Data are means 6 standard deviation.
† Data in parentheses are percentages.
‡ Data are proportions of total number of patients.
Radiology: Volume 264: Number 3—September 2012 n radiology.rsna.org 891
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
Figure 3
Figure 3: DFS curves for groups according to scores based on most frequently observed histologic sub-
type in consideration of two most predominant histologic subtypes.
Table 5
Correlation of Imaging Biomarker Features with Pathologic Grading System
and Scores
Biomarker Feature Pathologic Score*
Semiquantification of Tumor Components†
Central Fibrosis Grade 1 Grade 2 Grade 3
Tumor size 0.333§0.031 20.296§0.203‡0.192‡
1 cm 20.417 . . . 0.165 20.177 0.196
.1 cm 0.240§20.084 20.158 0.093 0.147
Tumor volume 0.215‡0.035 20.256§0.198‡0.169
Tumor mass 0.479§0.111 20.581§0.419§0.235§
SUVmax 0.559§0.043 20.491§0.289§0.350§
Note.—Data are R values (correlation coefficients).
* Based on two most predominant subtypes (10).
† Grade 1 = AIS; grade 2 = acinar and papillary patterns; and grade 3 = micropapillary and solid patterns (9).
‡ P , .05.
§ P , .01.
predominant grades was adopted for
patient prognosis determination, and
that scoring system showed good per-
formance in separating the groups for
the risk of tumor recurrence.
The identification of patients with
SPN lung adenocarcinoma who have a
higher risk for recurrence and who may
benefit from adjuvant therapy has been
a target of intense investigation. There
have been several reports suggesting that
tumor gene expression profiling has prog-
nostic relevance and can be used to help
predict disease recurrence (2,24–26).
Most of the gene expression studies rely
on microarray technology, which may
limit the practicability of this approach.
We found both the SUVmax at PET and
the mass of the nodule at CT help in the
categorization of patients, particularly
in those with high-grade tumors (more
solid tumor at CT). That result may sug-
gest the necessity of the combined use of
both the mass of the nodule and SUVmax
as preoperative imaging biomarkers (as
imaging prognostic surrogates).
It is also important to document
the subtypes of adenocarcinoma in
surgical pathologic reports, because
the subtypes BAC or AIS have been
shown to correlate with molecular ab-
normalities that help predict response
to targeted therapies (27,28). Recently,
Russell et al (29) reported that the new
classification has advantages not only
for individual patient care but also for
better selection and stratification of
clinical trials and molecular studies.
Our study had several limitations.
First, the small cohort size and different
proportions of each predominant pattern
limited our study; in the future, the re-
sults should be prospectively collected
and validated, ideally in population-based
studies. Second, the semiquantitative
approach to the assessment of adeno-
carcinomas that is described by the In-
ternational Association for the Study of
Lung Cancer, the American Thoracic
Society, and the European Respiratory
Society has yet to be validated further.
Until then, the international classification
system has been given a weak recom-
mendation with low-quality evidence
(10). Third, survival in patients with SPN
lung adenocarcinoma is not determined
by histopathologic subtypes and imag-
ing biomarker studies alone. Prognoses
may also be influenced by other factors
such as tumor stage, molecular features
such as epidermal growth factor recep-
tor and KRAS mutation positivity, and
treatment modalities including adjuvant
chemotherapeutic agents. Therefore,
prognosis for patients with SPN lung
adenocarcinomas may be determined
by integrating histopathologic subtypes,
imaging biomarker studies, tumor stage,
tumor molecular features, and given or
scheduled treatment methods. In our
study of 148 patients with clinical stage
1 SPN lung adenocarcinomas, 21 (14%)
892 radiology.rsna.org n Radiology: Volume 264: Number 3—September 2012
THORACIC IMAGING: Solitary Pulmonary Nodular Lung Adenocarcinoma Lee et al
6. Yim J, Zhu LC, Chiriboga L, Watson HN,
Goldberg JD, Moreira AL. Histologic fea-
tures are important prognostic indicators
in early stages lung adenocarcinomas. Mod
Pathol 2007;20(2):233–241.
7. Barletta JA, Yeap BY, Chirieac LR. Prognos-
tic significance of grading in lung adenocar-
cinoma. Cancer 2010;116(3):659–669.
8. Makimoto Y, Nabeshima K, Iwasaki H, et
al. Micropapillary pattern: a distinct path-
ological marker to subclassify tumours
with a significantly poor prognosis within
small peripheral lung adenocarcinoma
(,/=20 mm) with mixed bronchioloalveo-
lar and invasive subtypes (Noguchi’s type
C tumours). Histopathology 2005;46(6):
677–684.
9. Sica G, Yoshizawa A, Sima CS, et al. A grad-
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of lung cancer/american thoracic society/
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11. Lee HY, Han J, Lee KS, et al. Lung adeno-
carcinoma as a solitary pulmonary nodule:
prognostic determinants of CT, PET, and
est to disclose. H.J.K. No potential conflicts of
interest to disclose. J.H. No potential conflicts
of interest to disclose. B.T.K. No potential con-
flicts of interest to disclose. J.K. No potential
conflicts of interest to disclose. Y.M.S. No po-
tential conflicts of interest to disclose. J.H.K.
No potential conflicts of interest to disclose.
I.S. No potential conflicts of interest to disclose.
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Figure 4
Figure 4: Box and whisker plots show correlation between preoperative imaging biomarkers and postoperative pathologic scores in SPN of the lung. (a) Tumor
mass of the nodule volume at CT versus pathologic scores (P = .004), (b) SUVmax at PET/CT versus pathologic scores (P , .001). Post hoc Bonferroni comparisons
showed that all between-group differences were significant except those between scores 2 and 3 (P , .01 for nodule mass and P , .001 for SUVmax).
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