raylim commited on
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4ba0ba5
·
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1 Parent(s): 6241f9d

Update README with new Aeon inference parameters and add required CSV data files

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- Document new sex and tissue site parameters for improved Aeon inference
- Update all CSV examples to include optional Sex and Tissue Site columns
- Add required data files: sex_original_to_idx.csv, tissue_site_original_to_idx.csv, paladin_model_map.csv
- These files are needed for tests and application functionality

README.md CHANGED
@@ -141,13 +141,15 @@ mosaic --slide-csv /path/to/your/wsi_list.csv --output-dir /path/to/output/direc
141
  ```
142
 
143
  The CSV file should at least contain columns `Slide`, and `Site Type`.
144
- Optionally, it can also contain `Cancer Subtype`, `Segmentation Config`, and `IHC Subtype`.
145
 
146
  - `Slide` should contain the full path to the WSI file.
147
  - `Site Type` should be one of `Primary`, or `Metastatic`.
148
  - `Cancer Subtype` should be the oncotree code for the cancer subtype.
149
  - `Segmentation Config` should be one of `Biopsy`, `Resection`, or `TCGA`.
150
  - `IHC Subtype` should be one of `HR+/HER2+`, `HR+/HER2-`, `HR-/HER2+`, or `HR-/HER2-`.
 
 
151
 
152
  See additional options with the help command. This command may take a few seconds to run:
153
 
@@ -188,7 +190,9 @@ mosaic --slide-path /data/slides/sample.svs \
188
  --output-dir /data/results \
189
  --site-type Primary \
190
  --cancer-subtype Unknown \
191
- --segmentation-config Resection
 
 
192
  ```
193
 
194
  ### Example 2: Process a single breast cancer slide with known IHC subtype
@@ -199,7 +203,9 @@ mosaic --slide-path /data/slides/breast_sample.svs \
199
  --site-type Primary \
200
  --cancer-subtype BRCA \
201
  --ihc-subtype "HR+/HER2-" \
202
- --segmentation-config Biopsy
 
 
203
  ```
204
 
205
  ### Example 3: Process multiple slides from CSV
@@ -207,10 +213,10 @@ mosaic --slide-path /data/slides/breast_sample.svs \
207
  Create a CSV file `slides.csv` with the following format:
208
 
209
  ```csv
210
- Slide,Site Type,Cancer Subtype,Segmentation Config,IHC Subtype
211
- /data/slides/sample1.svs,Primary,Unknown,Resection,
212
- /data/slides/sample2.svs,Metastatic,LUAD,Biopsy,
213
- /data/slides/sample3.svs,Primary,BRCA,TCGA,HR+/HER2-
214
  ```
215
 
216
  Then run:
@@ -280,14 +286,24 @@ When processing multiple slides using the `--slide-csv` option, the CSV file mus
280
  - `HR+/HER2-`
281
  - `HR-/HER2+`
282
  - `HR-/HER2-`
 
 
 
 
 
 
 
 
 
 
283
 
284
  ### CSV Example
285
 
286
  ```csv
287
- Slide,Site Type,Cancer Subtype,Segmentation Config,IHC Subtype
288
- /data/slides/lung1.svs,Primary,LUAD,Resection,
289
- /data/slides/breast1.svs,Primary,BRCA,Biopsy,HR+/HER2-
290
- /data/slides/unknown1.svs,Metastatic,Unknown,TCGA,
291
  ```
292
 
293
  ## Cancer Subtypes
 
141
  ```
142
 
143
  The CSV file should at least contain columns `Slide`, and `Site Type`.
144
+ Optionally, it can also contain `Cancer Subtype`, `Segmentation Config`, `IHC Subtype`, `Sex`, and `Tissue Site`.
145
 
146
  - `Slide` should contain the full path to the WSI file.
147
  - `Site Type` should be one of `Primary`, or `Metastatic`.
148
  - `Cancer Subtype` should be the oncotree code for the cancer subtype.
149
  - `Segmentation Config` should be one of `Biopsy`, `Resection`, or `TCGA`.
150
  - `IHC Subtype` should be one of `HR+/HER2+`, `HR+/HER2-`, `HR-/HER2+`, or `HR-/HER2-`.
151
+ - `Sex` (optional): Patient sex for improved Aeon inference - one of `Male`, `Female`, or `Unknown`.
152
+ - `Tissue Site` (optional): Primary tissue site for improved Aeon inference (e.g., `Lung`, `Breast`, `Colon`, `Liver`, `Brain`).
153
 
154
  See additional options with the help command. This command may take a few seconds to run:
155
 
 
190
  --output-dir /data/results \
191
  --site-type Primary \
192
  --cancer-subtype Unknown \
193
+ --segmentation-config Resection \
194
+ --sex Female \
195
+ --tissue-site Lung
196
  ```
197
 
198
  ### Example 2: Process a single breast cancer slide with known IHC subtype
 
203
  --site-type Primary \
204
  --cancer-subtype BRCA \
205
  --ihc-subtype "HR+/HER2-" \
206
+ --segmentation-config Biopsy \
207
+ --sex Female \
208
+ --tissue-site Breast
209
  ```
210
 
211
  ### Example 3: Process multiple slides from CSV
 
213
  Create a CSV file `slides.csv` with the following format:
214
 
215
  ```csv
216
+ Slide,Site Type,Cancer Subtype,Segmentation Config,IHC Subtype,Sex,Tissue Site
217
+ /data/slides/sample1.svs,Primary,Unknown,Resection,,Female,Lung
218
+ /data/slides/sample2.svs,Metastatic,LUAD,Biopsy,,,Liver
219
+ /data/slides/sample3.svs,Primary,BRCA,TCGA,HR+/HER2-,Female,Breast
220
  ```
221
 
222
  Then run:
 
286
  - `HR+/HER2-`
287
  - `HR-/HER2+`
288
  - `HR-/HER2-`
289
+ - **Sex**: Patient sex for improved Aeon cancer subtype inference. One of `Male`, `Female`, or `Unknown`.
290
+ - **Tissue Site**: Primary tissue site for improved Aeon cancer subtype inference. Examples include:
291
+ - `Lung`
292
+ - `Breast`
293
+ - `Colon`
294
+ - `Liver`
295
+ - `Brain`
296
+ - `Lymph Node`
297
+ - `Bone`
298
+ - See `data/tissue_site_original_to_idx.csv` for complete list of supported tissue sites.
299
 
300
  ### CSV Example
301
 
302
  ```csv
303
+ Slide,Site Type,Cancer Subtype,Segmentation Config,IHC Subtype,Sex,Tissue Site
304
+ /data/slides/lung1.svs,Primary,LUAD,Resection,,Male,Lung
305
+ /data/slides/breast1.svs,Primary,BRCA,Biopsy,HR+/HER2-,Female,Breast
306
+ /data/slides/unknown1.svs,Metastatic,Unknown,TCGA,,,Liver
307
  ```
308
 
309
  ## Cancer Subtypes
data/paladin_model_map.csv ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ cancer_subtype,target_name,model_path
2
+ AASTR,IDH1.R132H,data/paladin/xf9lhkpd.epoch=12-step=13.model.pkl
3
+ AASTR,ATRX_TRUNC,data/paladin/nghmtwqq.epoch=12-step=13.model.pkl
4
+ AASTR,ATRX_ONCOGENIC,data/paladin/j3pgp96i.epoch=14-step=15.model.pkl
5
+ ADNOS,Del_17p,data/paladin/x474gdo3.epoch=16-step=51.model.pkl
6
+ ASTR,TP53_hotspot,data/paladin/ed73u1uo.epoch=7-step=16.model.pkl
7
+ ASTR,IDH1.R132H,data/paladin/7cxzdxfb.epoch=10-step=22.model.pkl
8
+ ASTR,ATRX_ONCOGENIC,data/paladin/gq60pnxn.epoch=9-step=20.model.pkl
9
+ ASTR,ATRX_TRUNC,data/paladin/0frn4tny.epoch=9-step=20.model.pkl
10
+ ASTR,TP53_ONCOGENIC,data/paladin/ww18g380.epoch=17-step=36.model.pkl
11
+ ASTR,IDH1_hotspot,data/paladin/uhfe7p8r.epoch=16-step=34.model.pkl
12
+ ASTR,IDH1_ONCOGENIC,data/paladin/3ar8pu9i.epoch=16-step=34.model.pkl
13
+ ASTR,RTK_RAS_PATHWAY,data/paladin/u91fy5r0.epoch=7-step=16.model.pkl
14
+ BLCA,Del_6q,data/paladin/eqtpu2m2.epoch=6-step=91.model.pkl
15
+ BLCA,TP53_PATHWAY,data/paladin/jaoqa2w3.epoch=7-step=104.model.pkl
16
+ BLCA,RB1_TRUNC,data/paladin/v5yz6ndf.epoch=7-step=104.model.pkl
17
+ BLCA,RB1_ONCOGENIC,data/paladin/54uvrvff.epoch=6-step=91.model.pkl
18
+ BLCA,FGFR3_ONCOGENIC,data/paladin/jchzcaq5.epoch=4-step=65.model.pkl
19
+ CCRCC,Del_14q,data/paladin/kr2wtldy.epoch=13-step=56.model.pkl
20
+ COAD,NKX3-1_HomDel,data/paladin/cwh3r34l.epoch=12-step=104.model.pkl
21
+ COAD,NKX3-1_ONCOGENIC,data/paladin/ji4ankxk.epoch=12-step=104.model.pkl
22
+ COAD,Amp_10p,data/paladin/9lt8fu1i.epoch=17-step=144.model.pkl
23
+ COAD,Del_5q,data/paladin/e3owivcb.epoch=11-step=96.model.pkl
24
+ COAD,BRAF_hotspot,data/paladin/gi1mikgg.epoch=19-step=300.model.pkl
25
+ COAD,Amp_13q,data/paladin/5fr6k4aq.epoch=2-step=45.model.pkl
26
+ COAD,Amp_17q,data/paladin/gs0nknp6.epoch=17-step=270.model.pkl
27
+ COAD,Del_18p,data/paladin/i9f8ace7.epoch=6-step=105.model.pkl
28
+ COAD,SRC_Amp,data/paladin/nz6n2i3j.epoch=13-step=210.model.pkl
29
+ COAD,Del_18q,data/paladin/46zyn9jd.epoch=5-step=48.model.pkl
30
+ COAD,ASXL1_Amp,data/paladin/jtjvnzv8.epoch=13-step=210.model.pkl
31
+ COAD,Del_21p,data/paladin/6m0qjb6j.epoch=16-step=255.model.pkl
32
+ COAD,KRAS_hotspot,data/paladin/9y9vj6vn.epoch=5-step=90.model.pkl
33
+ COAD,Del_21p,data/paladin/qih446gc.epoch=16-step=136.model.pkl
34
+ COAD,DNMT3B_Amp,data/paladin/3cb7sra7.epoch=14-step=225.model.pkl
35
+ COAD,SRC_ONCOGENIC,data/paladin/l4v77tuf.epoch=13-step=210.model.pkl
36
+ COAD,Del_18q,data/paladin/5x3oo7bu.epoch=6-step=105.model.pkl
37
+ COAD,Del_5q,data/paladin/aupfdkmm.epoch=8-step=135.model.pkl
38
+ COAD,TOP1_Amp,data/paladin/llb4wrf5.epoch=17-step=270.model.pkl
39
+ COAD,DNMT3B_ONCOGENIC,data/paladin/84q4qtj2.epoch=16-step=238.model.pkl
40
+ COAD,Amp_20q,data/paladin/lvgc5hf4.epoch=9-step=80.model.pkl
41
+ COAD,Amp_20q,data/paladin/3z7m1yvu.epoch=3-step=60.model.pkl
42
+ COAD,BRAF.V600E,data/paladin/hszhjtww.epoch=19-step=300.model.pkl
43
+ COAD,MSI_TYPE,data/paladin/7zkj9sa3.epoch=10-step=187.model.pkl
44
+ COADREAD,MSI_TYPE,data/paladin/ef9g2g05.epoch=19-step=80.model.pkl
45
+ GBM,TERT_ONCOGENIC,data/paladin/d2nctcc7.epoch=3-step=24.model.pkl
46
+ GBM,EGFR_Amp,data/paladin/3ym64o1g.epoch=12-step=78.model.pkl
47
+ GBM,EGFR_SV,data/paladin/c0piw4ym.epoch=14-step=90.model.pkl
48
+ GIST,EPIGENETIC_PATHWAY,data/paladin/imjphv1j.epoch=17-step=72.model.pkl
49
+ GIST,Del_10q,data/paladin/9c1quhjf.epoch=14-step=30.model.pkl
50
+ GIST,Del_1p,data/paladin/711sspdl.epoch=7-step=32.model.pkl
51
+ GIST,CELL_CYCLE_PATHWAY,data/paladin/tbty48me.epoch=13-step=56.model.pkl
52
+ GIST,CDKN2B_HomDel,data/paladin/bvo5yd3x.epoch=19-step=80.model.pkl
53
+ GIST,CDKN2B_ONCOGENIC,data/paladin/hchh1kxy.epoch=19-step=80.model.pkl
54
+ GIST,KIT_ONCOGENIC,data/paladin/5qv1l577.epoch=16-step=68.model.pkl
55
+ GIST,Del_9q,data/paladin/js5kwd8h.epoch=14-step=60.model.pkl
56
+ HCC,Del_8p,data/paladin/414aigjv.epoch=13-step=28.model.pkl
57
+ HGSOC,ZRSR2_HomDel,data/paladin/f49bti0v.epoch=18-step=152.model.pkl
58
+ HGSOC,ZRSR2_ONCOGENIC,data/paladin/w65puu1d.epoch=18-step=152.model.pkl
59
+ IDC,Del_14q,data/paladin/0332pa6g.epoch=4-step=80.model.pkl
60
+ IDC,NBN_Amp,data/paladin/3dckgf7z.epoch=19-step=320.model.pkl
61
+ IDC,Amp_10p,data/paladin/86dw519k.epoch=8-step=144.model.pkl
62
+ IDC,TP53_PATHWAY,data/paladin/nnpm1j02.epoch=5-step=96.model.pkl
63
+ IDC,Del_5q,data/paladin/qmd3pzkq.epoch=5-step=96.model.pkl
64
+ IDC,TP53_ONCOGENIC,data/paladin/vreyb1om.epoch=6-step=112.model.pkl
65
+ IDC,Amp_9p,data/paladin/03ag726b.epoch=18-step=304.model.pkl
66
+ LUAD,ALK_ONCOGENIC,data/paladin/ovyxd2ob.epoch=19-step=520.model.pkl
67
+ LUAD,CDKN2A_HomDel,data/paladin/jjzra532.epoch=8-step=243.model.pkl
68
+ LUAD,Del_8p,data/paladin/50r2qwax.epoch=4-step=135.model.pkl
69
+ LUAD,ALK_fusion,data/paladin/9yhryzl3.epoch=19-step=540.model.pkl
70
+ LUAD,EGFR.L858R,data/paladin/m5ryhg9a.epoch=10-step=297.model.pkl
71
+ LUAD,Amp_14q,data/paladin/ngn9k9im.epoch=10-step=297.model.pkl
72
+ LUAD,Del_10q,data/paladin/zb1foppw.epoch=15-step=432.model.pkl
73
+ LUAD,TP53_PATHWAY,data/paladin/1h5j6lcv.epoch=3-step=108.model.pkl
74
+ LUAD,Del_19p,data/paladin/eqkh5ixz.epoch=7-step=216.model.pkl
75
+ LUAD,STK11_TRUNC,data/paladin/k60w4up0.epoch=12-step=208.model.pkl
76
+ LUAD,EGFR_hotspot,data/paladin/8chbkvr2.epoch=3-step=108.model.pkl
77
+ LUAD,STK11_ONCOGENIC,data/paladin/0g4nz14y.epoch=6-step=112.model.pkl
78
+ LUAD,Del_21q,data/paladin/f1vh8j1d.epoch=11-step=324.model.pkl
79
+ LUAD,Amp_1q,data/paladin/nkik9rcn.epoch=6-step=189.model.pkl
80
+ LUAD,NRF2_PATHWAY,data/paladin/yqlfdrvs.epoch=8-step=243.model.pkl
81
+ LUAD,Del_21p,data/paladin/3szdaukp.epoch=16-step=459.model.pkl
82
+ LUAD,STK11_ONCOGENIC,data/paladin/uanm5qpb.epoch=6-step=189.model.pkl
83
+ LUSC,Del_10q,data/paladin/gbsuatlf.epoch=12-step=65.model.pkl
84
+ LUSC,Del_5q,data/paladin/otxp4y9q.epoch=15-step=80.model.pkl
85
+ LUSC,Del_3p,data/paladin/1gkyhld8.epoch=8-step=45.model.pkl
86
+ NBL,Del_Xp,data/paladin/7y3lp7vw.epoch=14-step=45.model.pkl
87
+ NBL,Del_Xq,data/paladin/ludvrs24.epoch=19-step=60.model.pkl
88
+ NBL,Amp_17q,data/paladin/qia84b7i.epoch=11-step=36.model.pkl
89
+ NBL,RTK_RAS_PATHWAY,data/paladin/wyd7ada7.epoch=0-step=2.model.pkl
90
+ NBL,MYCN_Amp,data/paladin/k88gw2io.epoch=14-step=45.model.pkl
91
+ NBL,MYCN_ONCOGENIC,data/paladin/sp7lpt88.epoch=14-step=45.model.pkl
92
+ NBL,MYC_PATHWAY,data/paladin/1onmwcqf.epoch=11-step=36.model.pkl
93
+ PAAD,Del_12p,data/paladin/cofs6tb2.epoch=14-step=210.model.pkl
94
+ PAAD,Del_15q,data/paladin/4366o7so.epoch=19-step=280.model.pkl
95
+ PAAD,MTAP_ONCOGENIC,data/paladin/h14fuggs.epoch=13-step=70.model.pkl
96
+ PAAD,CDKN2B_ONCOGENIC,data/paladin/c9onh7cc.epoch=5-step=54.model.pkl
97
+ PAAD,CDKN2B_HomDel,data/paladin/e6y1suye.epoch=5-step=54.model.pkl
98
+ PAAD,CDKN2A_HomDel,data/paladin/yci9dso2.epoch=5-step=54.model.pkl
99
+ PAAD,Del_21p,data/paladin/p3sgty9x.epoch=17-step=162.model.pkl
100
+ PANET,Amp_7q,data/paladin/8w0pcfqg.epoch=3-step=8.model.pkl
101
+ PANET,Amp_14q,data/paladin/kvqfszzv.epoch=11-step=24.model.pkl
102
+ PANET,Amp_17q,data/paladin/n05xwpfv.epoch=1-step=4.model.pkl
103
+ PANET,Del_2q,data/paladin/iway7gvk.epoch=19-step=40.model.pkl
104
+ PANET,Del_1q,data/paladin/nnv3biwl.epoch=14-step=30.model.pkl
105
+ PANET,Del_10p,data/paladin/ivcnueik.epoch=15-step=32.model.pkl
106
+ PANET,Del_3p,data/paladin/i18cwtml.epoch=10-step=22.model.pkl
107
+ PANET,Del_10q,data/paladin/lhdb8n6t.epoch=15-step=32.model.pkl
108
+ PANET,Del_2p,data/paladin/simlsmym.epoch=18-step=38.model.pkl
109
+ PANET,Del_3q,data/paladin/z9pxdil3.epoch=10-step=22.model.pkl
110
+ PRAD,Amp_11q,data/paladin/rlppqzob.epoch=19-step=300.model.pkl
111
+ PRAD,Del_15q,data/paladin/8y9cu8ti.epoch=5-step=90.model.pkl
112
+ PRAD,Amp_7q,data/paladin/4z9wd7oh.epoch=15-step=240.model.pkl
113
+ PRAD,SPOP_hotspot,data/paladin/tyqig7ov.epoch=14-step=225.model.pkl
114
+ SARCNOS,TP53_ONCOGENIC,data/paladin/xvpxs12a.epoch=12-step=39.model.pkl
115
+ SKCM,Del_10p,data/paladin/9dadxrr4.epoch=11-step=60.model.pkl
116
+ SKCM,Del_10q,data/paladin/f6l6c2v8.epoch=9-step=50.model.pkl
117
+ STAD,MSI_TYPE,data/paladin/kmi9hl2z.epoch=19-step=80.model.pkl
118
+ THPA,BRAF_hotspot,data/paladin/u0ec035p.epoch=13-step=28.model.pkl
119
+ UEC,ARID1A_ONCOGENIC,data/paladin/hrn4ehsm.epoch=11-step=108.model.pkl
120
+ UEC,Amp_10q,data/paladin/0kh0uesc.epoch=10-step=99.model.pkl
121
+ UEC,CTNNB1_ONCOGENIC,data/paladin/rmg2u8mw.epoch=12-step=26.model.pkl
122
+ UEC,CTNNB1_hotspot,data/paladin/0y5oa83k.epoch=13-step=28.model.pkl
123
+ UEC,KRAS_ONCOGENIC,data/paladin/wc3cv24o.epoch=12-step=117.model.pkl
124
+ UEC,Amp_10p,data/paladin/cpq494o0.epoch=18-step=171.model.pkl
125
+ UEC,TP53_hotspot,data/paladin/52dr57pb.epoch=14-step=135.model.pkl
126
+ UEC,TGF_BETA_PATHWAY,data/paladin/8uxv3vc8.epoch=17-step=162.model.pkl
127
+ UEC,Amp_8q,data/paladin/uzp4fgfa.epoch=14-step=135.model.pkl
128
+ UEC,Amp_1q,data/paladin/ce6wob99.epoch=14-step=135.model.pkl
129
+ UEC,MSI_TYPE,data/paladin/zrsfn6kz.epoch=13-step=126.model.pkl
130
+ UEC,CTNNB1_ONCOGENIC,data/paladin/lrfqme6d.epoch=11-step=108.model.pkl
131
+ UEC,CTNNB1_hotspot,data/paladin/hf3samzm.epoch=13-step=126.model.pkl
132
+ USC,Del_16q,data/paladin/eq6jer0f.epoch=9-step=30.model.pkl
133
+ UTUC,FGFR3_hotspot,data/paladin/qgajjknw.epoch=10-step=33.model.pkl
data/sex_original_to_idx.csv ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ idx,SEX
2
+ 0,Male
3
+ 0,MALE
4
+ 1,Female
5
+ 1,FEMALE
6
+ 2,Unknown
data/tissue_site_original_to_idx.csv ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ idx,TISSUE_SITE
2
+ 0,Lung
3
+ 1,Liver
4
+ 2,Lymph Node
5
+ 2,Lymph node
6
+ 2,"Lymph Node, Regional"
7
+ 3,Breast
8
+ 4,Colon
9
+ 4,Sigmoid Colon
10
+ 4,Ascending Colon
11
+ 4,Cecum
12
+ 4,Rectosigmoid Colon
13
+ 4,Transverse Colon
14
+ 4,Descending Colon
15
+ 5,Brain
16
+ 6,Bone
17
+ 6,Iliac Bone
18
+ 6,Spine
19
+ 6,Femur
20
+ 6,Rib
21
+ 7,Pancreas
22
+ 8,Not Applicable
23
+ 8,Not available
24
+ 9,Uterus
25
+ 10,Prostate
26
+ 11,Bladder
27
+ 12,Soft Tissue
28
+ 13,Ovary
29
+ 14,Rectum
30
+ 15,Kidney
31
+ 16,Skin
32
+ 17,Stomach
33
+ 18,Pleura
34
+ 19,Esophagus
35
+ 20,Peritoneum
36
+ 21,Omentum
37
+ 22,Retroperitoneum
38
+ 23,Pelvis
39
+ 24,Adrenal Gland
40
+ 24,Adrenal
41
+ 25,Endometrium
42
+ 26,Thyroid
43
+ 27,Chest Wall
44
+ 28,Abdomen
45
+ 29,Small Bowel
46
+ 29,Small Intestine
47
+ 30,GE Junction
48
+ 30,Gastroesophageal Junction
49
+ 31,Cervix
50
+ 32,Bile Duct
51
+ 33,Neck
52
+ 34,Mediastinum
53
+ 35,Testis
54
+ 36,Bowel
55
+ 37,Eye
56
+ 38,Thigh
57
+ 39,Vagina
58
+ 40,Pleural Fluid
59
+ 41,Upper Tract
60
+ 42,Axilla
61
+ 43,Diaphragm
62
+ 44,Gallbladder
63
+ 45,Abdominal Wall
64
+ 46,Appendix
65
+ 47,Anus
66
+ 48,Duodenum
67
+ 49,Renal Pelvis
68
+ 50,Tongue
69
+ 51,Mesentery
70
+ 52,Vulva
71
+ 53,Oral Cavity
72
+ 54,Fallopian tube
73
+ 55,Epidural
74
+ 56,Leg